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Xu, Xiaofeng, Ivor W. Tsang, and Chuancai Liu. "Improving Generalization via Attribute Selection on Out-of-the-Box Data." Neural Computation 32, no. 2 (February 2020): 485–514. http://dx.doi.org/10.1162/neco_a_01256.
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Zero-shot learning (ZSL) aims to recognize unseen objects (test classes) given some other seen objects (training classes) by sharing information of attributes between different objects. Attributes are artificially annotated for objects and treated equally in recent ZSL tasks. However, some inferior attributes with poor predictability or poor discriminability may have negative impacts on the ZSL system performance. This letter first derives a generalization error bound for ZSL tasks. Our theoretical analysis verifies that selecting the subset of key attributes can improve the generalization performance of the original ZSL model, which uses all the attributes. Unfortunately, previous attribute selection methods have been conducted based on the seen data, and their selected attributes have poor generalization capability to the unseen data, which is unavailable in the training stage of ZSL tasks. Inspired by learning from pseudo-relevance feedback, this letter introduces out-of-the-box data—pseudo-data generated by an attribute-guided generative model—to mimic the unseen data. We then present an iterative attribute selection (IAS) strategy that iteratively selects key attributes based on the out-of-the-box data. Since the distribution of the generated out-of-the-box data is similar to that of the test data, the key attributes selected by IAS can be effectively generalized to test data. Extensive experiments demonstrate that IAS can significantly improve existing attribute-based ZSL methods and achieve state-of-the-art performance.
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Ma, Xiaofei, Yi Feng, Yi Qu, and Yang Yu. "Attribute Selection Method based on Objective Data and Subjective Preferences in MCDM." International Journal of Computers Communications & Control 13, no. 3 (May 27, 2018): 391–407. http://dx.doi.org/10.15837/ijccc.2018.3.3188.
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Decision attributes are important parameters when choosing an alternative in a multiple criteria decision-making (MCDM) problem. In order to select the optimal set of decision attributes, an analysis framework is proposed to illustrate the attribute selection problem. Then a two-step attribute selection procedure is presented based on the framework: In the first step, attributes are filtered by using correlation algorithm. In the second step, a multi-objective optimization model is constructed to screen attributes from the results of the first step. Finally, a case study is given to illustrate and verify this method. The advantage of this method is that both external attribute data and subjective decision preferences are utilized in a sequential procedure. It enhances the reliability of decision attributes and matches the actual decision-making scenarios better.
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Zhao, Tao, Fangyu Li, and Kurt J. Marfurt. "Seismic attribute selection for unsupervised seismic facies analysis using user-guided data-adaptive weights." GEOPHYSICS 83, no. 2 (March 1, 2018): O31—O44. http://dx.doi.org/10.1190/geo2017-0192.1.
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With the rapid development in seismic attribute and interpretation techniques, interpreters can be overwhelmed by the number of attributes at their disposal. Pattern recognition-driven seismic facies analysis provides a means to identify subtle variations across multiple attributes that may only be partially defined on a single attribute. Typically, interpreters intuitively choose input attributes for multiattribute facies analysis based on their experience and the geologic target of interest. However, such an approach may overlook unsuspected or subtle features hidden in the data. We therefore augment this qualitative attribute selection process with quantitative measures of candidate attributes that best differentiate features of interest. Instead of selecting a group of attributes and assuming all the selected attributes contribute equally to the facies map, we weight the interpreter-selected input attributes based on their response from the unsupervised learning algorithm and the interpreter’s knowledge. In other words, we expect the weights to represent “which attribute is ‘favored’ by an interpreter as input for unsupervised learning” from an interpretation perspective and “which attribute is ‘favored’ by the learning algorithm” from a data-driven perspective. Therefore, we claim the weights are user guided and data adaptive, as the derivation of weight for each input attribute is embedded into the learning algorithm, providing a specific measurement tailored to the selected learning algorithm, while still taking the interpreter’s knowledge into account. We develop our workflow using Barnett Shale surveys and an unsupervised self-organizing map seismic facies analysis algorithm. We found that the proposed weighting-based attribute selection method better differentiates features of interest than using equally weighted input attributes. Furthermore, the weight values provide insights into dependency among input attributes.
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Zhang, Lin, and Jian Li Zhang. "Classification Algorithm Based on Category Attribute’s Mathematical Expectation." Advanced Materials Research 659 (January 2013): 103–7. http://dx.doi.org/10.4028/www.scientific.net/amr.659.103.
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The thesis introduced a classification algorithm- CAME which based on the training set’s mathematical expectation of each class attribute for unknown data. This algorithm converted the non-numerical or discrete attributes to the corresponding numerical data first, then calculate the mathematical expectation of data which belonging to different categories of numerical attributes. When a new data is needed to predict its classification, let each attribute’s mathematical expectation with existing categories as coordinate, then calculate the distance from new data attribute to various categories. The new data will belong to the category that has the shortest distance to the new data. This algorithm is not affected by attribute’s property or the number of category, and has high accuracy and good scalability.
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Duffy, Leanne M., and Shane P. Griffiths. "Assessing attribute redundancy in the application of productivity-susceptibility analysis to data-limited fisheries." Aquatic Living Resources 32 (2019): 20. http://dx.doi.org/10.1051/alr/2019018.
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Productivity-susceptibility analysis (PSA) is a widely used data-limited method to assess the relative vulnerability of species impacted by fisheries. Despite its widespread use, few authors have evaluated the impacts of attribute weightings and correlation of productivity attributes that may bias species' vulnerability scores. We evaluated the PSA methodology and performed sensitivity analyses to determine the impacts of correlation among productivity attributes used in the PSA, given that several of these attributes are strongly correlated. A PSA for species caught in the eastern Pacific Ocean tuna purse-seine fishery was used as an example to assess potential bias introduced by attribute weightings and correlation of productivity attributes on species' vulnerability scores. Redundancy was observed among three pairs of attributes. We demonstrated that manipulation of attribute weightings and removal of correlated attributes did not appreciably change any species' overall vulnerability status. Our results suggest that after removal of redundant attributes, PSAs can be conducted more rapidly with fewer data inputs than previous implementations, while retaining comparable vulnerability scores.
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Albattah, Waleed, Rehan Ullah Khan, and Khalil Khan. "Attributes Reduction in Big Data." Applied Sciences 10, no. 14 (July 17, 2020): 4901. http://dx.doi.org/10.3390/app10144901.
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Processing big data requires serious computing resources. Because of this challenge, big data processing is an issue not only for algorithms but also for computing resources. This article analyzes a large amount of data from different points of view. One perspective is the processing of reduced collections of big data with less computing resources. Therefore, the study analyzed 40 GB data to test various strategies to reduce data processing. Thus, the goal is to reduce this data, but not to compromise on the detection and model learning in machine learning. Several alternatives were analyzed, and it is found that in many cases and types of settings, data can be reduced to some extent without compromising detection efficiency. Tests of 200 attributes showed that with a performance loss of only 4%, more than 80% of the data could be ignored. The results found in the study, thus provide useful insights into large data analytics.
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Alfonseca, Enrique, Guillermo Garrido, Jean-Yves Delort, and Anselmo Peñas. "WHAD: Wikipedia historical attributes data." Language Resources and Evaluation 47, no. 4 (May 28, 2013): 1163–90. http://dx.doi.org/10.1007/s10579-013-9232-5.
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Zhao, Huimin. "Matching Attributes across Overlapping Heterogeneous Data Sources Using Mutual Information." Journal of Database Management 21, no. 4 (October 2010): 91–110. http://dx.doi.org/10.4018/jdm.2010100105.
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Identifying matching attributes across heterogeneous data sources is a critical and time-consuming step in integrating the data sources. In this paper, the author proposes a method for matching the most frequently encountered types of attributes across overlapping heterogeneous data sources. The author uses mutual information as a unified measure of dependence on various types of attributes. An example is used to demonstrate the utility of the proposed method, which is useful in developing practical attribute matching tools.
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Agrawal, Ankit, Sanchit Misra, Ramanathan Narayanan, Lalith Polepeddi, and Alok Choudhary. "Lung Cancer Survival Prediction using Ensemble Data Mining on Seer Data." Scientific Programming 20, no. 1 (2012): 29–42. http://dx.doi.org/10.1155/2012/920245.
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We analyze the lung cancer data available from the SEER program with the aim of developing accurate survival prediction models for lung cancer. Carefully designed preprocessing steps resulted in removal/modification/splitting of several attributes, and 2 of the 11 derived attributes were found to have significant predictive power. Several supervised classification methods were used on the preprocessed data along with various data mining optimizations and validations. In our experiments, ensemble voting of five decision tree based classifiers and meta-classifiers was found to result in the best prediction performance in terms of accuracy and area under the ROC curve. We have developed an on-line lung cancer outcome calculator for estimating the risk of mortality after 6 months, 9 months, 1 year, 2 year and 5 years of diagnosis, for which a smaller non-redundant subset of 13 attributes was carefully selected using attribute selection techniques, while trying to retain the predictive power of the original set of attributes. Further, ensemble voting models were also created for predicting conditional survival outcome for lung cancer (estimating risk of mortality after 5 years of diagnosis, given that the patient has already survived for a period of time), and included in the calculator. The on-line lung cancer outcome calculator developed as a result of this study is available at http://info.eecs.northwestern.edu:8080/LungCancerOutcomeCalculator/.
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Michelson, M., and C. A. Knoblock. "Creating Relational Data from Unstructured and Ungrammatical Data Sources." Journal of Artificial Intelligence Research 31 (March 28, 2008): 543–90. http://dx.doi.org/10.1613/jair.2409.
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In order for agents to act on behalf of users, they will have to retrieve and integrate vast amounts of textual data on the World Wide Web. However, much of the useful data on the Web is neither grammatical nor formally structured, making querying difficult. Examples of these types of data sources are online classifieds like Craigslist and auction item listings like eBay. We call this unstructured, ungrammatical data "posts." The unstructured nature of posts makes query and integration difficult because the attributes are embedded within the text. Also, these attributes do not conform to standardized values, which prevents queries based on a common attribute value. The schema is unknown and the values may vary dramatically making accurate search difficult. Creating relational data for easy querying requires that we define a schema for the embedded attributes and extract values from the posts while standardizing these values. Traditional information extraction (IE) is inadequate to perform this task because it relies on clues from the data, such as structure or natural language, neither of which are found in posts. Furthermore, traditional information extraction does not incorporate data cleaning, which is necessary to accurately query and integrate the source. The two-step approach described in this paper creates relational data sets from unstructured and ungrammatical text by addressing both issues. To do this, we require a set of known entities called a "reference set." The first step aligns each post to each member of each reference set. This allows our algorithm to define a schema over the post and include standard values for the attributes defined by this schema. The second step performs information extraction for the attributes, including attributes not easily represented by reference sets, such as a price. In this manner we create a relational structure over previously unstructured data, supporting deep and accurate queries over the data as well as standard values for integration. Our experimental results show that our technique matches the posts to the reference set accurately and efficiently and outperforms state-of-the-art extraction systems on the extraction task from posts.
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Mandru, Deena Babu, and Sundara Krishna Y.K. "Enhanced Cluster Ensemble Approach Using Multiple Attributes in Unreliable Categorical Data." International Journal of Psychosocial Rehabilitation 23, no. 1 (February 20, 2019): 254–63. http://dx.doi.org/10.37200/ijpr/v23i1/pr190235.
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I. Dimitras, Augustinos, Stelios Papadakis, and Alexandros Garefalakis. "Evaluation of empirical attributes for credit risk forecasting from numerical data." Investment Management and Financial Innovations 14, no. 1 (March 31, 2017): 9–18. http://dx.doi.org/10.21511/imfi.14(1).2017.01.
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In this research, the authors proposed a new method to evaluate borrowers’ credit risk and quality of financial statements information provided. They use qualitative and quantitative criteria to measure the quality and the reliability of its credit customers. Under this statement, the authors evaluate 35 features that are empirically utilized for forecasting the borrowers’ credit behavior of a Greek Bank. These features are initially selected according to universally accepted criteria. A set of historical data was collected and an extensive data analysis is performed by using non parametric models. Our analysis revealed that building simplified model by using only three out of the thirty five initially selected features one can achieve the same or slightly better forecasting accuracy when compared to the one achieved by the model uses all the initial features. Also, experimentally verified claim that universally accepted criteria can’t be globally used to achieve optimal results is discussed.
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Chen, Fan, Ruoqi Hu, Jiaoxiong Xia, and Jie Tao. "Processing on Structural Data Faultage in Data Fusion." Data 5, no. 1 (March 6, 2020): 21. http://dx.doi.org/10.3390/data5010021.
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With the rapid development of information technology, the development of information management system leads to the generation of heterogeneous data. The process of data fusion will inevitably lead to such problems as missing data, data conflict, data inconsistency and so on. We provide a new perspective that combines the theory in geology to conclude such kind of data errors as structural data faultage. Structural data faultages after data integration often lead to inconsistent data resources and inaccurate data information. In order to solve such problems, this article starts from the attributes of data. We come up with a new solution to process structural data faultages based on attribute similarity. We use the relation of similarity to define three new operations: Attribute cementation, Attribute addition, and Isomorphous homonuclear. Isomorphous homonuclear uses digraph to combine attributes. These three operations are mainly used to handle multiple data errors caused by data faultages, so that the redundancy of data can be reduced, and the consistency of data after integration can be ensured. Finally, it can eliminate the structural data faultage in data fusion. The experiment uses the data of doctoral dissertation in Shanghai University. Three types of dissertation data tables are fused. In addition, the structural data faultages after fusion are processed by the new method proposed by us. Through the statistical analysis of the experiment results and compare with the existing algorithm, we verify the validity and accuracy of this method to process structural data faultages.
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Yong-Xin, Zhang, Li Qing-Zhong, and Peng Zhao-Hui. "A novel method for data conflict resolution using multiple rules." Computer Science and Information Systems 10, no. 1 (2013): 215–35. http://dx.doi.org/10.2298/csis110613005y.
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In data integration, data conflict resolution is the crucial issue which is closely correlated with the quality of integrated data. Current research focuses on resolving data conflict on single attribute, which does not consider not only the conflict degree of different attributes but also the interrelationship of data conflict resolution on different attributes, and it can reduce the accuracy of resolution results. This paper proposes a novel two-stage data conflict resolution based on Markov Logic Networks. Our approach can divide attributes according to their conflict degree, then resolves data conflicts in the following two steps: (1)For the week conflicting attributes, we exploit a few common rules to resolve data conflicts, such rules as voting and mutual implication between facts. (2)Then, we resolve the strong conflicting attributes based on results from the first step. In this step, additional rules are added in rules set, such rules as inter-dependency between sources and facts, mutual dependency between sources and the influence of week conflicting attributes to strong conflicting attributes. Experimental results using a large number of real-world data collected from two domains show that the proposed approach can significantly improve the accuracy of data conflict resolution.
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Twala, Bhekisipho. "When Partly Missing Data Matters in Software Effort Development Prediction." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 5 (September 20, 2017): 803–12. http://dx.doi.org/10.20965/jaciii.2017.p0803.
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The major objective of the paper is to investigate a new probabilistic supervised learning approach that incorporates “missingness” into a decision tree classifier splitting criterion at each particular attribute node in terms of software effort development predictive accuracy. The proposed approach is compared empirically with ten supervised learning methods (classifiers) that have mechanisms for dealing with missing values. 10 industrial datasets are utilized for this task. Overall, missing incorporated in attributes 3 is the top performing strategy, followed by C4.5, missing incorporated in attributes, missing incorporated in attributes 2, missing incorporated in attributes, linear discriminant analysis and so on. Classification and regression trees and C4.5 performed well in data with high correlations among attributes whilek-nearest neighbour and support vector machines performed well in data with higher complexity (limited number of instances). The worst performing method is repeated incremental pruning to produce error reduction.
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Trevanich, Anothai, and Patrick Bourke. "EWMA Control Charts Using Attributes Data." Statistician 42, no. 3 (1993): 215. http://dx.doi.org/10.2307/2348797.
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Mampaey, Michael, and Jilles Vreeken. "Summarizing categorical data by clustering attributes." Data Mining and Knowledge Discovery 26, no. 1 (November 26, 2011): 130–73. http://dx.doi.org/10.1007/s10618-011-0246-6.
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Yu, Ping, Wei Ni, Guangsheng Yu, Hua Zhang, Ren Ping Liu, and Qiaoyan Wen. "Efficient Anonymous Data Authentication for Vehicular Ad Hoc Networks." Security and Communication Networks 2021 (February 22, 2021): 1–14. http://dx.doi.org/10.1155/2021/6638453.
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Vehicular ad hoc network (VANET) encounters a critical challenge of efficiently and securely authenticating massive on-road data while preserving the anonymity and traceability of vehicles. This paper designs a new anonymous authentication approach by using an attribute-based signature. Each vehicle is defined by using a set of attributes, and each message is signed with multiple attributes, enabling the anonymity of vehicles. First, a batch verification algorithm is developed to accelerate the verification processes of a massive volume of messages in large-scale VANETs. Second, replicate messages captured by different vehicles and signed under different sets of attributes can be dereplicated with the traceability of all the signers preserved. Third, the malicious vehicles forging data can be traced from their signatures and revoked from attribute groups. The security aspects of the proposed approach are also analyzed by proving the anonymity of vehicles and the unforgeability of signatures. The efficiency of the proposed approach is numerically verified, as compared to the state of the art.
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Shu, Wenhao, Wenbin Qian, Yonghong Xie, and Zhaoping Tang. "An Efficient Uncertainty Measure-based Attribute Reduction Approach for Interval-valued Data with Missing Values." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27, no. 06 (December 2019): 931–47. http://dx.doi.org/10.1142/s0218488519500417.
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Attribute reduction plays an important role in knowledge discovery and data mining. Confronted with data characterized by the interval and missing values in many data analysis tasks, it is interesting to research the attribute reduction for interval-valued data with missing values. Uncertainty measures can supply efficient viewpoints, which help us to disclose the substantive characteristics of such data. Therefore, this paper addresses the attribute reduction problem based on uncertainty measure for interval-valued data with missing values. At first, an uncertainty measure is provided for measuring candidate attributes, and then an efficient attribute reduction algorithm is developed for the interval-valued data with missing values. To improve the efficiency of attribute reduction, the objects that fall within the positive region are deleted from the whole object set in the process of selecting attributes. Finally, experimental results demonstrate that the proposed algorithm can find a subset of attributes in much shorter time than existing attribute reduction algorithms without losing the classification performance.
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Cichosz, Paweł. "Urban Crime Risk Prediction Using Point of Interest Data." ISPRS International Journal of Geo-Information 9, no. 7 (July 21, 2020): 459. http://dx.doi.org/10.3390/ijgi9070459.
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Geographical information systems have found successful applications to prediction and decision-making in several areas of vital importance to contemporary society. This article demonstrates how they can be combined with machine learning algorithms to create crime prediction models for urban areas. Selected point of interest (POI) layers from OpenStreetMap are used to derive attributes describing micro-areas, which are assigned crime risk classes based on police crime records. POI attributes then serve as input attributes for learning crime risk prediction models with classification learning algorithms. The experimental results obtained for four UK urban areas suggest that POI attributes have high predictive utility. Classification models using these attributes, without any form of location identification, exhibit good predictive performance when applied to new, previously unseen micro-areas. This makes them capable of crime risk prediction for newly developed or dynamically changing neighborhoods. The high dimensionality of the model input space can be considerably reduced without predictive performance loss by attribute selection or principal component analysis. Models trained on data from one area achieve a good level of prediction quality when applied to another area, which makes it possible to transfer or combine crime risk prediction models across different urban areas.
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Dewett, Dustin T., John D. Pigott, and Kurt J. Marfurt. "A review of seismic attribute taxonomies, discussion of their historical use, and presentation of a seismic attribute communication framework using data analysis concepts." Interpretation 9, no. 3 (August 1, 2021): B39—B64. http://dx.doi.org/10.1190/int-2020-0222.1.
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Beginning in the 1970s, seismic attributes have grown from a few simple measurements of wavelet amplitude, frequency, and phase to an expanded attribute toolbox that measures not only wavelet properties but also their context within the 3D seismic volume. When using multiple seismic attributes, the interpreter must understand not only each individual attribute but also the relationships between them. Researchers communicate these relationships via seismic attribute taxonomies, which group attributes by their signal property, mathematical formulation, or their interpretive value. The first attempts to organize seismic attributes began in the 1990s, and with new attributes and their increasing breadth of applications, continues to this day. Most scientific papers that use seismic attributes focus on a specific application, new algorithms, or a novel interpretation workflow, rather than how a specific attribute fits within the greater whole, leading to confusion for the less experienced interpreter. We have analyzed more than 2100 citing works, identified the 231 papers that discuss the taxonomies specifically, and found out how the authors use those citations. The result is a list of more than a dozen seismic attribute classification systems, which we reduce to a smaller subset by including only those that apply to general use. An optimal seismic attribute taxonomy should not only be useful to the interpretation community today, but it should also adapt to the ever-changing needs of the profession, including changes appropriate for their use in modern machine-learning algorithms. The adaptability of prior work to modern workflows remains a shortcoming. However, as we develop our work in two parts — the first covering the evolution of seismic attribute taxonomies and their use through time and the second proposing a new seismic attribute communication framework for the larger community — we link attributes together via data analysis principles and provide an extensible model as the profession and research expand.
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Yuan, Sanyi, Jinghan Wang, Tao Liu, Tao Xie, and Shangxu Wang. "6D phase-difference attributes for wide-azimuth seismic data interpretation." GEOPHYSICS 85, no. 6 (November 1, 2020): IM37—IM49. http://dx.doi.org/10.1190/geo2019-0431.1.
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Phase information of seismic signals is sensitive to subsurface discontinuities. However, 1D phase attributes are not robust when dealing with noisy data. In addition, variations of seismic phase attributes with azimuth are seldom explored. To address these issues, we have developed 6D phase-difference attributes (PDAs) derived from azimuthal phase-frequency spectra. For the seismic volume of a certain azimuth and frequency, we first construct stacked phase traces at each common-depth point along a certain decomposed trending direction. Then, the 6D PDA is extracted by calculating the complex-valued covariance at a 6D phase space. The proposed method enables characterization of the subsurface discontinuities and indicates seismic anisotropy. Moreover, we provide one q-value attribute obtained by singular value decomposition to describe the variation intensity of PDA along different azimuths. Simulated and field wide-azimuth seismic data sets are used to illustrate the performance of the proposed 6D PDA and the derived q-value attribute. The results show that PDA at different frequencies can image various geologic features, including faults, fracture groups, and karst caves. Our field data example shows that PDA is able to discern the connectivity of karst caves using large-azimuth data. In addition, PDA along different azimuths and the q-value attribute provide a measurement of azimuthal variations, as well as the anisotropy. Our 6D PDA method can be used as a potential tool for wide-azimuth seismic data interpretation.
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Fu, Liwei, and Sen Wu. "An Internal Clustering Validation Index for Boolean Data." Cybernetics and Information Technologies 16, no. 6 (December 1, 2016): 232–44. http://dx.doi.org/10.1515/cait-2016-0091.
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Abstract Internal clustering validation is recognized as one of the vital issues essential to clustering applications, especially when external information is not available. Existing measures have their limitations in different application circumstances. There are still some deficiencies for Internal Validation of Boolean clustering. This paper proposes a new Clustering Validation index based on Type of Attributes for Boolean data (CVTAB). It evaluates the clustering quality in the light of Dissimilarity of two clusters for Boolean Data (DBD). The attributes in the Boolean Data are categorized into three types: Type A, Type O and Type E representing respectively the attribute values 1,0 and not the same for all the objects in the set. When two clusters are composed into one, DBD applies the numbers of attributes with the types changed and the numbers of objects changed to measure dissimilarity of two clusters. CVTAB evaluates the clustering quality without respect to external information
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Liu, Hui, Jingqing Jiang, Yaowei Hou, and Jie Song. "Solving the Fragment Complexity of Official, Social, and Sensorial Urban Data." Complexity 2020 (October 14, 2020): 1–14. http://dx.doi.org/10.1155/2020/8914757.
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Cities in the big data era hold the massive urban data to create valuable information and digitally enhanced services. Sources of urban data are generally categorized as one of the three types: official, social, and sensorial, which are from the government and enterprises, social networks of citizens, and the sensor network. These types typically differ significantly from each other but are consolidated together for the smart urban services. Based on the sophisticated consolidation approaches, we argue that a new challenge, fragment complexity that represents a well-integrated data has appropriate but fragmentary schema and difficult to be queried, is ignored in the state-of-art urban data management. Comparing with predefined and rigid schema, fragmentary schema means a dataset contains millions of attributes but nonorthogonally distributed among tables, and of course, values of these attributes are even massive. As far as a query is concerned, locating where these attributes are being stored is the first encountered problem, while traditional value-based query optimization has no contributions. To address this problem, we propose an index on massive attributes as an attributes-oriented optimization, namely, attribute index. Attribute index is a secondary index for locating files in which the target attributes are stored. It contains three parts: ATree for searching keys, DTree for locating keys among files, and ADLinks as a mapping table between ATree and DTree. In this paper, the index architecture, logical structure and algorithms, the implementation details, the creation process, the integration to the existing key-value store, and the urban application scenario are described. Experiments show that, in comparison with B + -Tree, LSM-Tree, and AVL-Tree, the query time of ATree is 1.1x, 1.5x, and 1.2x faster, respectively. Finally, we integrate our proposition with HBase, namely, UrbanBase, whose query performance is 1.3x faster than the original HBase.
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Battaglia, Elena, Simone Celano, and Ruggero G. Pensa. "Differentially Private Distance Learning in Categorical Data." Data Mining and Knowledge Discovery 35, no. 5 (July 13, 2021): 2050–88. http://dx.doi.org/10.1007/s10618-021-00778-0.
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AbstractMost privacy-preserving machine learning methods are designed around continuous or numeric data, but categorical attributes are common in many application scenarios, including clinical and health records, census and survey data. Distance-based methods, in particular, have limited applicability to categorical data, since they do not capture the complexity of the relationships among different values of a categorical attribute. Although distance learning algorithms exist for categorical data, they may disclose private information about individual records if applied to a secret dataset. To address this problem, we introduce a differentially private family of algorithms for learning distances between any pair of values of a categorical attribute according to the way they are co-distributed with the values of other categorical attributes forming the so-called context. We define different variants of our algorithm and we show empirically that our approach consumes little privacy budget while providing accurate distances, making it suitable in distance-based applications, such as clustering and classification.
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Scheevel, J. R., and K. Payrazyan. "Principal Component Analysis Applied to 3D Seismic Data for Reservoir Property Estimation." SPE Reservoir Evaluation & Engineering 4, no. 01 (February 1, 2001): 64–72. http://dx.doi.org/10.2118/69739-pa.
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Summary We apply a common statistical tool, Principal Component Analysis (PCA) to the problem of direct property estimation from three-dimensional (3D) seismic-amplitude data. We use PCA in a novel way to successfully make detailed effective porosity predictions in channelized sand and shale. The novelty of this use revolves around the sampling method, which consists of a small vertical sampling window applied by sliding along each vertical trace in a cube of seismic-amplitude data. The window captures multiple, vertically adjacent amplitude samples, which are then treated as vectors for purposes of the PCA analysis. All vectors from all sample window locations within the seismic-data volume form the set of input vectors for the PCA algorithm. Final output from the PCA algorithm can be a cube of assigned classes, whose clustering is based on the values of the most significant principal components (PC's). The clusters are used as a categorical variable when predicting reservoir properties away from well control. The novelty in this approach is that PCA analysis is used to analyze covariance relationships between all vector elements (neighboring amplitude values) by using the statistical mass of the large number of vectors sampled in the seismic data set. Our approach results in a powerful signal-analysis method that is statistical in nature. We believe it offers data-driven objectivity and a potential for property extraction not easily achieved in model-driven fourier-based time-series methods of analysis (digital signal processing). We evaluate the effectiveness of our method by applying a cross-validation technique, alternately withholding each of the three wells drilled in the area and computing predicted effective porosity (PHIE) estimates at the withheld location by using the remaining two wells as hard data. This process is repeated three times, each time excluding only one of the wells as a blind control case. In each of the three blind control wells, our method predicts accurate estimates of sand/shale distribution in the well and effective porosity-thickness product values. The method properly predicts a low sand-to-shale ratio at the blind well location, even when the remaining two hard data wells contain only high sand-to-shale ratios. Good predictive results from this study area make us optimistic that the method is valuable for general reservoir property prediction from 3D seismic data, especially in areas of rapid lateral variation of the reservoir. We feel that this method of predicting properties from the 3D seismic is preferable to traditional, solely variogram-based geostatistical estimation methods. Such methods have difficulty capturing the detailed lithology distribution when limited by the hard data control's sampling bias. This problem is especially acute in areas where rapid lateral geological variation is the rule. Our method effectively overcomes this limitation because it provides a deterministic soft template for reservoir-property distributions. Introduction Reservoir Prediction from Seismic. The use of the reflection seismic-attribute data for the prediction of detailed reservoir properties began at least as early as 1969.1 Use of seismic attributes for reservoir prediction has accelerated in recent years, especially with the advent of widely available high-quality 3D seismic data. In practice, a seismic attribute is any property derived from the seismic reflection (amplitude) signal during or after final processing. Any attributes may be compared with a primary reservoir property or lithology in an attempt to devise a method of attribute-guided prediction of the primary property away from well control. The prediction method can vary from something as simple as a linear multiplier (single attribute) to multi-attribute analysis with canonical correlation techniques,2 geostatistical methods,3 or fully nonlinear, fuzzy methods.4 The pace of growth in prediction methodologies using seismic attributes seems to be outpaced only by the proliferation in the number and types of seismic attributes reported in the literature.5 As more researchers find predictive success with one or more new attributes, the list of viable reservoir-predictive attributes continues to grow. Chen and Sidney6 have cataloged more than 60 common seismic attributes along with a description of their apparent significance and use. Despite the rich history of seismic attribute in reservoir prediction, the practice remains difficult and uncertain. The bulk of this uncertainty arises from the unclear nature of the physics connecting many demonstrably useful attributes to a corresponding reservoir property. Because of the complex and varied physical processes responsible for various attributes, the unambiguous use of attributes for direct reservoir prediction will likely remain a challenge for years to come. In addition to the questions about the physical origin of some attributes, there is the possibility of encountering statistical pitfalls while using multiple attributes for empirical reservoir-property prediction. For example, it has been demonstrated that as the number of attributes used in an evaluation increases, the potential arises that one or more attributes will produce a false correlation with well data.7 Also, many attributes are derived with similar signal-processing methods and can, in some cases, be considered largely redundant with respect to their seismic-signal description. Lendzionowski et al.8 maintain that the maximum number of independent attributes required to fully describe a trace segment is a quantity 2BT, where B=bandwidth (Hz) and T=trace-segment length (sec). If this is supportable, it suggests that most of the more common attributes are at least partially redundant. The danger of such redundancy is that it falsely enhances statistical correlation with the well property. Doing so may suggest that many seemingly independent seismic attributes display similar well-property trends. Finally, the use of a particular approach with attributes involves some subjectivity and prior experience on the part of the practitioner to be successful and reproducible. This is a source of potential error that cannot be quantified but also, in most cases, cannot be avoided. The most successful workers in the field of reservoir prediction from seismic, not coincidentally, are also the most experienced in the field.
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Chen, Zhihong. "Data Covariance Learning in Aesthetic Attributes Assessment." Journal of Applied Mathematics and Physics 08, no. 12 (2020): 2869–79. http://dx.doi.org/10.4236/jamp.2020.812212.
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Lakshmanan, Valliappa, and Travis Smith. "Data Mining Storm Attributes from Spatial Grids." Journal of Atmospheric and Oceanic Technology 26, no. 11 (November 1, 2009): 2353–65. http://dx.doi.org/10.1175/2009jtecha1257.1.
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Abstract A technique to identify storms and capture scalar features within the geographic and temporal extent of the identified storms is described. The identification technique relies on clustering grid points in an observation field to find self-similar and spatially coherent clusters that meet the traditional understanding of what storms are. From these storms, geometric, spatial, and temporal features can be extracted. These scalar features can then be data mined to answer many types of research questions in an objective, data-driven manner. This is illustrated by using the technique to answer questions of forecaster skill and lightning predictability.
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Dong, Lei, Carlo Ratti, and Siqi Zheng. "Predicting neighborhoods’ socioeconomic attributes using restaurant data." Proceedings of the National Academy of Sciences 116, no. 31 (July 15, 2019): 15447–52. http://dx.doi.org/10.1073/pnas.1903064116.
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Accessing high-resolution, timely socioeconomic data such as data on population, employment, and enterprise activity at the neighborhood level is critical for social scientists and policy makers to design and implement location-based policies. However, in many developing countries or cities, reliable local-scale socioeconomic data remain scarce. Here, we show an easily accessible and timely updated location attribute—restaurant—can be used to accurately predict a range of socioeconomic attributes of urban neighborhoods. We merge restaurant data from an online platform with 3 microdatasets for 9 Chinese cities. Using features extracted from restaurants, we train machine-learning models to estimate daytime and nighttime population, number of firms, and consumption level at various spatial resolutions. The trained model can explain 90 to 95% of the variation of those attributes across neighborhoods in the test dataset. We analyze the tradeoff between accuracy, spatial resolution, and number of training samples, as well as the heterogeneity of the predicted results across different spatial locations, demographics, and firm industries. Finally, we demonstrate the cross-city generality of this method by training the model in one city and then applying it directly to other cities. The transferability of this restaurant model can help bridge data gaps between cities, allowing all cities to enjoy big data and algorithm dividends.
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Liu, Wan Jin, Jin Chao An, Hui Zhou, and Chao Su. "Application to Sedimentary Facies Identification Used RGB Fusion Imaging in Multi-Attribute." Advanced Materials Research 546-547 (July 2012): 656–60. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.656.
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The seismic attribute has multi-solution, and can not correspond to geological bodies exactly, a variety of seismic attributes information interpreted by changes in their characteristic parameters was prone to conflicts, the fusion technology of multi-attribute fuses the independent single-attribute in seismic data together, it can use the advantage of each attribute to display the characterization of geological body vividly. In this paper, we extract the attributes slice under the control of isochronous stratigraphic framework along layers, optimize the attribute using reference well data to select three independent attributes that can reflect lithological and physical properties, and fuse the three favorable attributes using the image of RGB fusion technology for better identification of sedimentary facies.
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Zhu, Yadi, Feng Chen, Ming Li, and Zijia Wang. "Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data." Sustainability 10, no. 11 (November 13, 2018): 4178. http://dx.doi.org/10.3390/su10114178.
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Socioeconomic attributes are essential characteristics of people, and many studies on economic attribute inference focus on data that contain user profile information. For data without user profiles, like smart card data, there is no validated method for inferring individual economic attributes. This study aims to bridge this gap by formulating a mobility to attribute framework to infer passengers’ economic attributes based on the relationship between individual mobility and personal attributes. This framework integrates shop consumer prices, house prices, and smart card data using three steps: individual mobility extraction, location feature identification, and economic attribute inference. Each passenger’s individual mobility is extracted by smart card data. Economic features of stations are described using house price and shop consumer price data. Then, each passenger’s comprehensive consumption indicator set is formulated by integrating these data. Finally, individual economic levels are classified. From the case study of Beijing, commuting distance and trip frequency using the metro have a negative correlation with passengers’ income and the results confirm that metro passengers are mainly in the low- and middle-income groups. This study improves on passenger information extracted from data without user profile information and provides a method to integrate multisource big data mining for more information.
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Mittal, Ruchi. "Multivariate Regression Predictive Modeling in Analyzing Student Performance: A Data Mining Approach." Journal of Computational and Theoretical Nanoscience 16, no. 10 (October 1, 2019): 4362–66. http://dx.doi.org/10.1166/jctn.2019.8526.
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This study is based on student performance in the measured in terms of their academic grades. The performance is measured based on a number of demographic, behavioral and historical attributes. To analyze the result and to create a predictive model, the study uses the data mining technique of multivariate regression and correlation attribute evaluation on a dataset of 395 respondents drawn from an academic program covering the subject of mathematics in a school in Portugal. A total of eight predictor variables are used to predict the criterion variable. The criterion or the dependent variable is final grades. Out the eight attributes, five attributes i.e., “student’s age,” “quality of family relationship,” “going out with friends” and “current health status” are significant predictors of “final grades” whereas three attributes i.e., “free time after school,” “work day alcohol consumption,” “weekend alcohol consumption” and “number of school absences” are insignificant as far as the regression model is concerned.
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Xiao, Yuelei, and Haiqi Li. "Privacy Preserving Data Publishing for Multiple Sensitive Attributes Based on Security Level." Information 11, no. 3 (March 22, 2020): 166. http://dx.doi.org/10.3390/info11030166.
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Privacy preserving data publishing has received considerable attention for publishing useful information while preserving data privacy. The existing privacy preserving data publishing methods for multiple sensitive attributes do not consider the situation that different values of a sensitive attribute may have different sensitivity requirements. To solve this problem, we defined three security levels for different sensitive attribute values that have different sensitivity requirements, and given an L s l -diversity model for multiple sensitive attributes. Following this, we proposed three specific greed algorithms based on the maximal-bucket first (MBF), maximal single-dimension-capacity first (MSDCF) and maximal multi-dimension-capacity first (MMDCF) algorithms and the maximal security-level first (MSLF) greed policy, named as MBF based on MSLF (MBF-MSLF), MSDCF based on MSLF (MSDCF-MSLF) and MMDCF based on MSLF (MMDCF-MSLF), to implement the L s l -diversity model for multiple sensitive attributes. The experimental results show that the three algorithms can greatly reduce the information loss of the published microdata, but their runtime is only a small increase, and their information loss tends to be stable with the increasing of data volume. And they can solve the problem that the information loss of MBF, MSDCF and MMDCF increases greatly with the increasing of sensitive attribute number.
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Zhang, Xiao Lei, and Yi Tang. "Protecting Encrypted Data against Inference Attacks in Outsourced Databases." Applied Mechanics and Materials 571-572 (June 2014): 621–25. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.621.
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Ensuring data privacy and improving query performance are two closely linked challenges for outsourced databases. Using mixed encryption methods to data attributes can reach an explicit trade-off between these two challenges. However, encryption cannot always conceal relations between attributes values. When the data tuples are accessed selectively, inferences based on comparing encrypted values could be launched and sensitive values may be disclosed. In this paper, we explore the attribute based inferences in mixed encrypted databases. We develop a method to construct private indexes on encrypted values to defend against inference while supporting efficient selective access to encrypted data. We have conducted some experiments to validate our proposed method.
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Ren, Xiangmin, Boxuan Jia, and Kechao Wang. "Anatomy: Uncertain Data k-Anonymity Privacy Protection Algorithm." Applied Mechanics and Materials 433-435 (October 2013): 1689–92. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.1689.
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Uncertain data management has become an important research direction and a hot area of research. This paper proposes an UDAK-anonymity algorithm via anatomy for relational uncertain data. Uncertain data influence matrix based on background knowledge is built in order to describe the influence degree of sensitive attribute and Quasi-identifier (QI) attributes. We use generalization and BK(L,K)-clustering to present equivalent class, L makes sensitive attributes diversity in one equivalent class. Experimental results show that UDAK-anonymity algorithm are utility, effective and efficient, and can make anonymous uncertainty data effectively resist background knowledge attack and homogeneity attack.
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Ram Prasad Reddy, S., K. VSVN Raju, and V. Valli Kumari. "A Novel Approach for Personalized Privacy Preserving Data Publishing with Multiple Sensitive Attributes." International Journal of Engineering & Technology 7, no. 2.20 (April 18, 2018): 197. http://dx.doi.org/10.14419/ijet.v7i2.20.13296.
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The Personalized Privacy has drawn a lot of attention from diverse magnitudes of the public and various functional units like bureau of statistics, and hospitals. A large number of data publishing models and methods have been proposed and most of them focused on single sensitive attribute. A few research papers marked the need for preserving privacy of data consisting of multiple sensitive attributes. Applying the existing methods such as k-anonymity, l-diversity directly for publishing multiple sensitive attributes would minimize the utility of the data. Moreover, personalization has not been studied in this dimension. In this paper, we present a publishing model that manages personalization for publishing data with multiple sensitive attributes. The model uses slicing technique supported by deterministic anonymization for quasi identifiers; generalization for categorical sensitive attributes; and fuzzy approach for numerical sensitive attributes based on diversity. We cap the belief of an adversary inferring a sensitive value in a published data set to as high as that of an inference based on public knowledge. The experiments were carried out on census dataset and synthetic datasets. The results ensure that the privacy is being safeguarded without any compromise on the utility of the data.
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Thachaparambil, Manoj Vallikkat. "Discrete 3D fracture network extraction and characterization from 3D seismic data — A case study at Teapot Dome." Interpretation 3, no. 3 (August 1, 2015): ST29—ST41. http://dx.doi.org/10.1190/int-2014-0219.1.
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Three-dimensional discrete fracture networks (DFNs) extracted from the seismic data of the Tensleep Formation at Teapot Dome successfully matched 1D fracture data from multiple boreholes within the area. The extraction process used four seismic attributes, i.e., variance, chaos, curvature, and spectral edge, and their multiple realizations to define seismic discontinuities that could potentially represent fractures within the Tensleep Formation. All of the potential fracture attributes were further enhanced using a fracture-tracking attribute for better extraction and analysis of seismic discontinuity surfaces and their network properties. A state-of-the-art discontinuity surface extraction and characterization workflow uniformly extracted and interactively characterized the seismic discontinuity surfaces and networks that correlate with borehole fracture data. Among the attributes, a fracture-tracking attribute cube created out of the high-resolution spectral-edge attribute provided the best match with the borehole fracture data from the Tensleep Formation. Therefore, the extracted discontinuity planes were classified as fractures and then characterized. The extracted fracture population also matched earlier published records of faults and fractures at Teapot Dome. Unlike the conventional method, which uses 1D borehole fracture data as primary input and 3D seismic data as a guide volume during DFN modeling, I used 3D seismic attributes as the primary data and the 1D borehole fracture data only for quality control. I also evaluated the power of converting seismic fracture attribute volumes into discrete surfaces and networks for effective correlation with 1D fracture logs from boreholes.
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Christopher, A. B. Arockia, and S. Appavu alias Balamurugan. "Prediction of warning level in aircraft accidents using data mining techniques." Aeronautical Journal 118, no. 1206 (August 2014): 935–52. http://dx.doi.org/10.1017/s0001924000009623.
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Abstract Data mining is a data analysis process which is designed for large amounts of data. It proposes a methodology for evaluating risk and safety and describes the main issues of aircraft accidents. We have a huge amount of knowledge and data collection in aviation companies. This paper focuses on different feature selectwindion techniques applied to the datasets of airline databases to understand and clean the dataset. CFS subset evaluator, consistency subset evaluator, gain ratio feature evaluator, information gain attribute evaluator, OneR attribute evaluator, principal components attribute transformer, ReliefF attribute evaluatoboundar and symmetrical uncertainty attribute evaluator are used in this study in order to reduce the number of initial attributes. The classification algorithms, such as DT, KNN, SVM, NN and NB, are used to predict the warning level of the component as the class attribute. We have explored the use of different classification techniques on aviation components data. For this purpose Weka software tools are used. This study also proves that the principal components attribute with decision tree classifier would perform better than other attributes and techniques on airline data. Accuracy is also very highly improved. This work may be useful for an aviation company to make better predictions. Some safety recommendations are also addressed to airline companies.
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Dorrington, Kevin P., and Curtis A. Link. "Genetic‐algorithm/neural‐network approach to seismic attribute selection for well‐log prediction." GEOPHYSICS 69, no. 1 (January 2004): 212–21. http://dx.doi.org/10.1190/1.1649389.
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Neural‐network prediction of well‐log data using seismic attributes is an important reservoir characterization technique because it allows extrapolation of log properties throughout a seismic volume. The strength of neural‐networks in the area of pattern recognition is key in its success for delineating the complex nonlinear relationship between seismic attributes and log properties. We have found that good neural‐network generalization of well‐log properties can be accomplished using a small number of seismic attributes. This study presents a new method for seismic attribute selection using a genetic‐algorithm approach. The genetic algorithm attribute selection uses neural‐network training results to choose the optimal number and type of seismic attributes for porosity prediction. We apply the genetic‐algorithm attribute‐selection method to the C38 reservoir in the Stratton field 3D seismic data set. Eleven wells with porosity logs are used to train a neural network using genetic‐algorithm selected‐attribute combinations. A histogram of 50 genetic‐algorithm attribute selection runs indicates that amplitude‐based attributes are the best porosity predictors for this data set. On average, the genetic algorithm selected four attributes for optimal porosity log prediction, although the number of attributes chosen ranged from one to nine. A predicted porosity volume was generated using the best genetic‐algorithm attribute combination based on an average cross‐validation correlation coefficient. This volume suggested a network of channel sands within the C38 reservoir.
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Ahmed, Osama A., Radwan E. Abdel-Aal, and Husam AlMustafa. "Reservoir property prediction using abductive networks." GEOPHYSICS 75, no. 1 (January 2010): P1—P9. http://dx.doi.org/10.1190/1.3298443.
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Statistical methods, such as linear regression and neural networks, are commonly used to predict reservoir properties from seismic attributes. However, a huge number of attributes can be extracted from seismic data and an efficient method for selecting an attribute subset with the highest correlation to the property being predicted is essential. Most statistical methods, however, lack an optimized approach for this attribute selection. We propose to predict reservoir properties from seismic attributes using abductive networks, which use iterated polynomial regression to derive high-degree polynomial predictors. The abductive networks simultaneously select the most relevant attributes and construct an optimal nonlinear predictor. We applied the approach to predict porosity from seismic data of an area within the 'Uthmaniyah portion of the Ghawar oil field, Saudi Arabia. The data consisted of normal seismic amplitude, acoustic impedance, 16 other seismic attributes, and porosity logs from seven wells located in the study area. Out of 27 attributes, the abductive network selected only the best two to six attributes and produced a more accurate and robust porosity prediction than using the more common neural-network predictors. In addition, the proposed method requires no effort in choosing the attribute subset or tweaking their parameters.
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Wan, Hong Xin, and Yun Peng. "A Fuzzy Clustering Algorithm of Web Customers Based on Attributes Reduction." Advanced Materials Research 989-994 (July 2014): 1775–78. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1775.
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The evaluation algorithm is based on the attributes of data objects. There is a certain correlation between attributes, and attributes are divided into key attributes and secondary attributes. This paper proposes an algorithm of attribute reduction based on rough set and the clustering algorithm based on fuzzy set. The algorithm of attributes reduction based on rough set is described in detail first. There are a lot of uncertain data of customer clustering, so traditional method of classification to the incomplete data will be very complex. Clustering algorithm based on fuzzy set can improve the reliability and accuracy of web customers.
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Et. al., S. Noordeen ,. "Cloud Data Security in Multi Model Attributes For Randomized Key Service Using Encryption Techniques." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 437–49. http://dx.doi.org/10.17762/turcomat.v12i2.832.
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The growing size of information, security became the risk of access from centralized resource providers deploy them into cloud, where authorized users could access them. The nearly combined nature of cloud environment does not yet allow users to perceive the cloud resources and services in a split second. At the same time, they are upgrading. Also, attribute based encryption techniques are used towards the security development in few methods. Also, profile based approaches are used which uses different encryption keys according to user profile. However, all the methods suffer to achieve higher performance in datasecurity. To solve this issue and to improve the security performance, an efficient Service LevelAttributeBasedEncryption (SLABE)ispresented.Inthisapproach,themethoduses different key set of different services. For each attribute, the method maintains different keys for various services. According to the key belongs to the attributes and service, the method performs encryption and decryption. The method improves the performance in security and increases the throughput aswell. Further to improve the security performance, a multi attribute randomized key Service Level Encryption (MARK-SLE) scheme has been presented. In this approach, the method classifies the service and for each service, the method generates different key set according to the attributes accessed. The method selects the keys in a randomized approach and chooses the keys at different time session. Generated key has been used to perform encryption or decryption where the schemes of encryption also selected in a random manner. The proposed MARK-SLE algorithm improves the security performance than previous SLABEalgorithm. Third, a Service Level Scheduler Based Encryption (SLSBE) Scheme is presented. In this approach, the security in service level and scheduling strategy is considered. For each service available, the method maintains the set of attributes being accessed. For each level of service and attributes, the method uses different keys and encryption standards. At the reception of user request, the method identifies the service claimed and set of attributes.
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Causse, E., M. Riede, A. J. van Wijngaarden, A. Buland, J. F. Dutzer, and R. Fillon. "Amplitude analysis with an optimal model-based linear AVO approximation: Part II — Field data example." GEOPHYSICS 72, no. 3 (May 2007): C71—C79. http://dx.doi.org/10.1190/1.2712176.
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AVO analysis can be conducted by estimating amplitude variation with offset (AVO) attributes from seismic prestack data and by characterizing the measured amplitude responses by the position of their projection in the attribute space. The most common AVO attributes are the intercept (zero-offset reflectivity) and AVO gradient. We have constructed an optimized, model-based linear AVO equation that is more accurate than usual AVO approximations. The parameters of this equation represent new AVO attributes that are more directly related to the information contained in seismic reflection amplitudes. We use these new AVO attributes to classify reflector responses from field data. Five seismic facies are defined that are characterized by differentdistributions of seismic parameters. Nine reflector classes are formed by associating appropriate pairs of facies. The expected locations of the different reflector classes in the space of optimized attributes are found by modeling and are used to derive a classification scheme. This scheme is applied to sections of optimized attributes calculated from the prestack data, leading to a vertical section showing the distribution of most probable facies in an area containing a sand reservoir. We compare the new approach to classification with intercept and gradient. The new method is more robust and less sensitive to the number of attributes (two or three) used for classification. It offers an optimal, flexible, and robust way of extracting the information contained in reflection amplitudes by simple linear AVO equations.
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Gebreslasie, M. T., F. B. Ahmed, and Jan A. N. van Aardt. "Predicting forest structural attributes using ancillary data and ASTER satellite data." International Journal of Applied Earth Observation and Geoinformation 12 (February 2010): S23—S26. http://dx.doi.org/10.1016/j.jag.2009.11.006.
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Attaullah, Hasina, Adeel Anjum, Tehsin Kanwal, Saif Ur Rehman Malik, Alia Asheralieva, Hassan Malik, Ahmed Zoha, Kamran Arshad, and Muhammad Ali Imran. "F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes." Sensors 21, no. 14 (July 20, 2021): 4933. http://dx.doi.org/10.3390/s21144933.
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With the advent of smart health, smart cities, and smart grids, the amount of data has grown swiftly. When the collected data is published for valuable information mining, privacy turns out to be a key matter due to the presence of sensitive information. Such sensitive information comprises either a single sensitive attribute (an individual has only one sensitive attribute) or multiple sensitive attributes (an individual can have multiple sensitive attributes). Anonymization of data sets with multiple sensitive attributes presents some unique problems due to the correlation among these attributes. Artificial intelligence techniques can help the data publishers in anonymizing such data. To the best of our knowledge, no fuzzy logic-based privacy model has been proposed until now for privacy preservation of multiple sensitive attributes. In this paper, we propose a novel privacy preserving model F-Classify that uses fuzzy logic for the classification of quasi-identifier and multiple sensitive attributes. Classes are defined based on defined rules, and every tuple is assigned to its class according to attribute value. The working of the F-Classify Algorithm is also verified using HLPN. A wide range of experiments on healthcare data sets acknowledged that F-Classify surpasses its counterparts in terms of privacy and utility. Being based on artificial intelligence, it has a lower execution time than other approaches.
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Brown, Alistair R. "Understanding seismic attributes." GEOPHYSICS 66, no. 1 (January 2001): 47–48. http://dx.doi.org/10.1190/1.1444919.
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With the success of 3-D surveys has come the popularity of seismic attributes. Attributes are valuable for gaining insight from the data particularly when displayed spatially over interpreted horizons. However, all the many attributes available are not independent pieces of information but, in fact, simply represent different ways of presenting a limited amount of basic information. The key to success lies in selecting the most applicable attribute for the problem. Furthermore, statistical analysis using attributes must be based on understanding, not simply mathematical correlation.
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Jinyin, Chen, He Huihao, Chen Jungan, Yu Shanqing, and Shi Zhaoxia. "Fast Density Clustering Algorithm for Numerical Data and Categorical Data." Mathematical Problems in Engineering 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/6393652.
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Data objects with mixed numerical and categorical attributes are often dealt with in the real world. Most existing algorithms have limitations such as low clustering quality, cluster center determination difficulty, and initial parameter sensibility. A fast density clustering algorithm (FDCA) is put forward based on one-time scan with cluster centers automatically determined by center set algorithm (CSA). A novel data similarity metric is designed for clustering data including numerical attributes and categorical attributes. CSA is designed to choose cluster centers from data object automatically which overcome the cluster centers setting difficulty in most clustering algorithms. The performance of the proposed method is verified through a series of experiments on ten mixed data sets in comparison with several other clustering algorithms in terms of the clustering purity, the efficiency, and the time complexity.
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Sadik-Rozsnyai, Orsolya, and Laurent Bertrandias. "New technological attributes and willingness to pay: the role of social innovativeness." European Journal of Marketing 53, no. 6 (June 10, 2019): 1099–124. http://dx.doi.org/10.1108/ejm-12-2016-0834.
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PurposeIntegrating new technological attributes into existing products is a common way to innovate and is supposed to meet consumers’ functional needs. This paper aims to demonstrate how adding such attributes also increases willingness to pay (WTP) a premium for a product by activating consumers’ social need to feel unique.Design/methodology/approachThe data were collected through a quantitative survey based on a nationally representative sample (N= 345). A choice-based conjoint analysis was used to estimate the perceived value of the new technological attribute and WTP a premium.FindingsThe perceived value of the new technological attribute has a positive effect on WTP a premium only for consumers with a high degree of social innovativeness (linked to their need for uniqueness) because they interpret this innovation as an opportunity to differentiate themselves from others.Practical implicationsWhen companies innovate by introducing new technological attributes, their communication should emphasize and trigger these attributes’ high performance and uniqueness. Thus, consumers seeking social differentiation through innovation will be much less sensitive to price and will be more prone to pay a premium for these products.Originality/valueThe main contribution of this article is to show that integrating and emphasizing a new technological attribute can increase consumers’ WTP a premium beyond that of the attribute’s functional value. Thus, new technological attributes will decrease the price sensitivity of consumers high in social innovativeness and increase their WTP a premium for the product, because they consider it as a means to stand out from others.
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Khomsah, Siti, and Edi Winarko. "Pemanfaatan Algoritma WIT-Tree dan HITS untuk Klasifikasi Tingkat Keberhasilan Pemberdayaan Keluarga Miskin." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 11, no. 1 (January 31, 2017): 31. http://dx.doi.org/10.22146/ijccs.15927.
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The successful rate of the poor families empowerment can be classified by characteristic patterns extracted from the database that contains the data of the poor families empowerment. The purpose of this research is to build a classification model to predict the level of success from poor families, who will receive assistance empowerment of poverty. Classification models built with WARM, which is combining two methods, they are HITS and WIT-tree. HITS is used to obtained the weight of the attributes from the database. The weights are used as the attributes’s weight on methods WIT-tree. WIT-tree is used to generate the association rules that satisfy a minimum weight support and minimum weight confidence. The data used was 831 sample data poor families that divided into two classes, namely poor families in the standard of "developing" and poor families in the level of "underdeveloped". The performance of classification model shows, weighting attribute using HITS approaches the accuracy of 86.45% and weighted attributes defined by the user approaches the accuracy of 66.13%. This study shows that the weight of the attributes obtained from HITS is better than the weight of the attributes specified by the user.
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Avseth, Per, and Tor Veggeland. "Seismic screening of rock stiffness and fluid softening using rock-physics attributes." Interpretation 3, no. 4 (November 1, 2015): SAE85—SAE93. http://dx.doi.org/10.1190/int-2015-0054.1.
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We have developed a methodology to create easy-to-implement rock-physics attributes that can be used to screen for reservoir sandstones and hydrocarbon pore fill from seismic inversion data. Most seismic attributes are based on the empirical relationships between reservoir properties and seismic observables. We have honored the physical properties of the rocks by defining attributes that complied with calibrated rock-physics models. These attributes included the fluid saturation sensitive curved pseudo-elastic impedance (CPEI) and the rock stiffness/lithology attribute pseudo-elastic impedance for lithology (PEIL). We found that the CPEI attribute correlated nicely with saturation and resistivity, whereas the PEIL attribute in practice was a scaled version of the shear modulus and correlated nicely with porosity. We determined the use of these attributes on well log and seismic inversion data from the Norwegian Sea, and we successfully screened out reservoir rocks filled with either water or hydrocarbons.