Relevant bibliographies by topics / Feature set partitioning

Contents

  1. Journal articles
  2. Books
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Feature set partitioning'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Feature set partitioning.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Feature set partitioning"

1

Rokach, Lior. "Genetic algorithm-based feature set partitioning for classification problems." Pattern Recognition 41, no. 5 (2008): 1676–700. http://dx.doi.org/10.1016/j.patcog.2007.10.013.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Matatov, Nissim, Lior Rokach, and Oded Maimon. "Privacy-preserving data mining: A feature set partitioning approach." Information Sciences 180, no. 14 (2010): 2696–720. http://dx.doi.org/10.1016/j.ins.2010.03.011.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Lawrence, Ogange. "Hybridizing grid partitioning, rough set theory, and feature selection for fuzzy rule generation in dataset classification." International Journal of Enterprise Modelling 17, no. 2 (2023): 26–34. http://dx.doi.org/10.35335/emod.v13i1.20.

Full text
Abstract:
This research investigates the hybridization of Grid Partitioning, Rough Set Theory, and Feature Selection for Fuzzy Rule Generation in Dataset Classification. The objective is to improve classification accuracy and interpretability by integrating multiple techniques. Grid partitioning is employed to divide the dataset into regions, allowing localized analysis. Rough set theory is utilized for attribute reduction and feature selection, identifying informative features within each region. Fuzzy rule generation is applied to generate interpretable classification rules using linguistic terms and
APA, Harvard, Vancouver, ISO, and other styles
4

Chelly Dagdia, Zaineb, and Christine Zarges. "A Detailed Study of the Distributed Rough Set Based Locality Sensitive Hashing Feature Selection Technique." Fundamenta Informaticae 182, no. 2 (2021): 111–79. http://dx.doi.org/10.3233/fi-2021-2069.

Full text
Abstract:
In the context of big data, granular computing has recently been implemented by some mathematical tools, especially Rough Set Theory (RST). As a key topic of rough set theory, feature selection has been investigated to adapt the related granular concepts of RST to deal with large amounts of data, leading to the development of the distributed RST version. However, despite of its scalability, the distributed RST version faces a key challenge tied to the partitioning of the feature search space in the distributed environment while guaranteeing data dependency. Therefore, in this manuscript, we pr
APA, Harvard, Vancouver, ISO, and other styles
5

Mert, Ahmet, Niyazi Kılıç, Erdem Bilgili, and Aydin Akan. "Breast Cancer Detection with Reduced Feature Set." Computational and Mathematical Methods in Medicine 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/265138.

Full text
Abstract:
This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such ask-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC wit
APA, Harvard, Vancouver, ISO, and other styles
6

Kumar, Aditya, and Jainath Yadav. "Improving multi-view ensemble learning with Round-Robin feature set partitioning." Data & Knowledge Engineering 156 (March 2025): 102380. http://dx.doi.org/10.1016/j.datak.2024.102380.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Han, Sunwoo, and Hyunjoong Kim. "Optimal Feature Set Size in Random Forest Regression." Applied Sciences 11, no. 8 (2021): 3428. http://dx.doi.org/10.3390/app11083428.

Full text
Abstract:
One of the most important hyper-parameters in the Random Forest (RF) algorithm is the feature set size used to search for the best partitioning rule at each node of trees. Most existing research on feature set size has been done primarily with a focus on classification problems. We studied the effect of feature set size in the context of regression. Through experimental studies using many datasets, we first investigated whether the RF regression predictions are affected by the feature set size. Then, we found a rule associated with the optimal size based on the characteristics of each data. La
APA, Harvard, Vancouver, ISO, and other styles
8

Tahmoresnezhad, Jafar, and Sattar Hashemi. "An Efficient yet Effective Random Partitioning and Feature Weighting Approach for Transfer Learning." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 02 (2016): 1651003. http://dx.doi.org/10.1142/s0218001416510034.

Full text
Abstract:
One of the serious challenges in machine learning and pattern recognition is to transfer knowledge from related but different domains to a new unlabeled domain. Feature selection with maximum mean discrepancy (f-MMD) is a novel and effective approach to transfer knowledge from source domain (training set) into target domain (test set) where training and test sets are drawn from different distributions. However, f-MMD has serious challenges in facing datasets with large number of samples and features. Moreover, f-MMD ignores the feature-label relation in finding the reduced representation of da
APA, Harvard, Vancouver, ISO, and other styles
9

Jinnai, Yuu, and Alex Fukunaga. "On Hash-Based Work Distribution Methods for Parallel Best-First Search." Journal of Artificial Intelligence Research 60 (October 30, 2017): 491–548. http://dx.doi.org/10.1613/jair.5225.

Full text
Abstract:
Parallel best-first search algorithms such as Hash Distributed A* (HDA*) distribute work among the processes using a global hash function. We analyze the search and communication overheads of state-of-the-art hash-based parallel best-first search algorithms, and show that although Zobrist hashing, the standard hash function used by HDA*, achieves good load balance for many domains, it incurs significant communication overhead since almost all generated nodes are transferred to a different processor than their parents. We propose Abstract Zobrist hashing, a new work distribution method for para
APA, Harvard, Vancouver, ISO, and other styles
10

Abubakullo, Abubakullo, and Aisyah Alesha. "Exploring the synergistic effects of hybrid grid partitioning and rough set method for fuzzy rule generation in dataset classification." International Journal of Enterprise Modelling 17, no. 2 (2023): 1–13. http://dx.doi.org/10.35335/emod.v17i2.18.

Full text
Abstract:
This research explores the synergistic effects of hybrid grid partitioning and the rough set method for fuzzy rule generation in dataset classification. The aim is to improve the accuracy and interpretability of the classification process. The rough set-based feature selection technique is employed to identify the most relevant features for classification, leading to a focused and informative feature subset. The hybrid grid partitioning approach combines clustering algorithms and grid-based methods to create an efficient grid structure, capturing the intrinsic data distribution. This enhances
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Feature set partitioning"

1

Metcalf, Michael, John Reid, and Malcolm Cohen. Fortran 2018 coarray enhancements. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198811893.003.0020.

Full text
Abstract:
Teams allow separate sets of images to execute independently. An important design objective is that, given code that has been developed and tested on all images, it should be possible to run the code on a team without making changes. This requires that if a team has n images, the image indices within the team run from 1 to n. Teams are formed by partitioning an existing team into parts, starting with the team of all the images. New teams are executed within change team constructs. Most execution will be within the team, but direct access to data in ancestor and sibling teams is allowed. The nu
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Feature set partitioning"

1

Singh, Ritika, and Vipin Kumar. "A comprehensive analysis of multi-view feature-set partitioning methods with machine learning algorithms." In Intelligent Computing and Communication Techniques. CRC Press, 2025. https://doi.org/10.1201/9781003635680-45.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Riyazuddin, MD, and V. V. S. S. S. Balaram. "Pattern Anonymization: Hybridizing Data Restructure with Feature Set Partitioning for Privacy Preserving in Supervised Learning." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2471-9_58.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Shevchenko, Tetyana, Elena Kiseleva, and Larysa Koriashkina. "The Features of Solving of the set Partitioning Problems with Moving Boundaries Between Subsets." In Operations Research Proceedings 2008. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00142-0_86.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Déchaine, Rose-Marie. "Partitioning the nominal domain." In Gender and Noun Classification. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198828105.003.0002.

Full text
Abstract:
Class qua gender is a mechanism for introducing a partition, i.e. noun classes, into the nominal domain. Treating class/gender as a partition function—a function that exhaustively and non-intersectively assigns all members of a set to a subset—provides insight into the range of variation attested in natural languages relative to the realization of class/gender. This set-theoretic analysis is embedded in a model of interface syntax that allows a given feature’s modus and locus of association to vary. Specifically, the locus of association of class/gender varies such that it can associate with a
APA, Harvard, Vancouver, ISO, and other styles
5

Qin, Meng. "A Software Code Infringement Detection Scheme Based on Integration Learning." In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde231264.

Full text
Abstract:
A software code plagiarism detection scheme based on ensemble learning is designed to address the issue of low accuracy in traditional abstract syntax tree based software code infringement detection methods. We adopt the AST structure of the code to integrate domain partitioning in IR with AST, and use a weighted simplified abstract syntax tree to design feature extraction and similarity calculation methods, to achieve partial detection of semantic plagiarism and calculate the similarity between text and source code. Then, the feature set of the known classification training set is placed into
APA, Harvard, Vancouver, ISO, and other styles
6

Gkolemis, Vasilis, Theodore Dalamagas, Eirini Ntoutsi, and Christos Diou. "RHALE: Robust and Heterogeneity-Aware Accumulated Local Effects." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230354.

Full text
Abstract:
Accumulated Local Effects (ALE) is a widely-used explainability method for isolating the average effect of a feature on the output, because it handles cases with correlated features well. However, it has two limitations. First, it does not quantify the deviation of instance-level (local) effects from the average (global) effect, known as heterogeneity. Second, for estimating the average effect, it partitions the feature domain into user-defined, fixed-sized bins, where different bin sizes may lead to inconsistent ALE estimations. To address these limitations, we propose Robust and Heterogeneit
APA, Harvard, Vancouver, ISO, and other styles
7

Tidke, Sonali. "MonogDB." In Privacy and Security Policies in Big Data. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2486-1.ch004.

Full text
Abstract:
MongoDB is a NoSQL type of database management system which does not adhere to the commonly used relational database management model. MongoDB is used for horizontal scaling across a large number of servers which may have tens, hundreds or even thousands of servers. This horizontal scaling is performed using sharding. Sharding is a database partitioning technique which partitions large database into smaller parts which are easy to manage and faster to access. There are hundreds of NoSQL databases available in the market. But each NoSQL product is different in terms of features, implementations
APA, Harvard, Vancouver, ISO, and other styles
8

Mazzella, L., M. C. Buia, M. C. Gambi, et al. "Plant-animal trophic relationships In the Posidonia oceanica ecosystem of the Mediterranean Sea: a review." In Plant-Animal Interactions in the Marine Benthos. Oxford University PressOxford, 1992. http://dx.doi.org/10.1093/oso/9780198577546.003.0008.

Full text
Abstract:
Abstract In the Mediterranean Sea, beds of the endemic phanerogam Posidonia oceanica, characterized by both high subsurface and high photosynthetic biomass, form one of the most productive systems of the basin. The biomass values can exceed those of other phanerogams and the plant shows distinct partitioning, often mainly directed into the lignified rhizomes, which can account for up to 90 per cent of total biomass. Leafborne algal flora can at times contribute up to 30 per cent of overall production. Part of the energy produced by the plant is exported to adjacent systems through leaf detritu
APA, Harvard, Vancouver, ISO, and other styles
9

Bonabeau, Eric, Marco Dorigo, and Guy Theraulaz. "Cemetery Organization, Brood Sorting, Data Analysis, and Graph Partitioning." In Swarm Intelligence. Oxford University Press, 1999. http://dx.doi.org/10.1093/oso/9780195131581.003.0008.

Full text
Abstract:
In the previous two chapters, foraging and division of labor were shown to be useful metaphors to design optimization and resource allocation algrithms. In this chapter, we will see that the clustering and sorting behavior of ants has stimulated researchers to design new algorithms for data analysis and graph partitioning. Several species of ants cluster corpses to form a “cemetery,” or sort their larvae into several piles. This behavior is still not fully understood, but a simple model, in which agents move randomly in space and pick up and deposit items on the basis of local information, may
APA, Harvard, Vancouver, ISO, and other styles
10

Griffith, Daniel A., and Larry J. Layne. "Concluding Comments." In A Casebook For Spatial Statistical Data Analysis. Oxford University PressNew York, NY, 1999. http://dx.doi.org/10.1093/oso/9780195109580.003.0010.

Full text
Abstract:
Abstract Much of the emphasis in this book is on commonalities shared by geostatistical and spatial autoregressive methodologies: the pivotal concept of spatial autocorrelation, a principal linkage provided by the missing data problem, the dual relationship between spatial correlograms and partial correlograms, and the MC (Moran Coefficient) scatterplot and the semivariogram plot visualization tools. This is a theme that other spatial scientists are beginning to address, too (see Cressie, et al., 1999). One salient feature differentiating geostatistics from spatial autoregression is that the f
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Feature set partitioning"

1

Chang, Xiaojing, Yan Yang, and Hongiun Wang. "Multi-view Construction for Clustering Based on Feature set Partitioning." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489615.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Kim, Yong Se. "Form Feature Recognition by Convex Decomposition." In ASME 1991 International Computers in Engineering Conference and Exposition. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/cie1991-0009.

Full text
Abstract:
Abstract A convex decomposition method, called Alternating Sum of Volumes (ASV), uses convex hulls and set difference operations. ASV decomposition may not converge, which severely limits the domain of geometric objects that can be handled. By combining ASV decomposition and remedial partitioning for the non-convergence, we have proposed a convergent convex decomposition called Alternating Sum of Volumes with Partitioning (ASVP). In this article, we describe how ASVP decomposition is used for recognition of form features. ASVP decomposition can be viewed as a hierarchical volumetric representa
APA, Harvard, Vancouver, ISO, and other styles
3

Parienté, Frédéric, and Yong Se Kim. "Augmented Convex Decomposition Using Incremental Update for Recognition of Form Features." In ASME 1996 Design Engineering Technical Conferences and Computers in Engineering Conference. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/96-detc/cie-1342.

Full text
Abstract:
Abstract Alternating Sum of Volumes with Partitioning (ASVP) decomposition is a volumetric representation of a part obtained from its boundary representation that organizes faces of the part in an outside-in hierarchy. ASVP decomposition combines Alternating Sum of Volumes (ASV) decomposition, using convex hulls and set difference operations, and remedial partitioning, using cutting operations and concave edges. A Form Feature Decomposition (FFD) which can serve as a central feature representation for various applications is obtained from ASVP decomposition. The incremental update of convex de
APA, Harvard, Vancouver, ISO, and other styles
4

Losi, Enzo, Mauro Venturini, Lucrezia Manservigi, et al. "Data Selection and Feature Engineering for the Application of Machine Learning to the Prediction of Gas Turbine Trip." In ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/gt2021-58914.

Full text
Abstract:
Abstract A gas turbine trip is an unplanned shutdown, of which the consequences are business interruption and a reduction of equipment remaining useful life. Therefore, detection and identification of symptoms of trips would allow predicting its occurrence, thus avoiding damages and costs. The development of machine learning models able to predict gas turbine trip requires the definition of a set of target data and a procedure of feature engineering that improves machine learning generalization and effectiveness. This paper presents a methodology that focuses on the steps that precede the deve
APA, Harvard, Vancouver, ISO, and other styles
5

Yao, Quanming, Xiawei Guo, James Kwok, et al. "Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/571.

Full text
Abstract:
To meet the standard of differential privacy, noise is usually added into the original data, which inevitably deteriorates the predicting performance of subsequent learning algorithms. In this paper, motivated by the success of improving predicting performance by ensemble learning, we propose to enhance privacy-preserving logistic regression by stacking. We show that this can be done either by sample-based or feature-based partitioning. However, we prove that when privacy-budgets are the same, feature-based partitioning requires fewer samples than sample-based one, and thus likely has better e
APA, Harvard, Vancouver, ISO, and other styles
6

Holla, V. Devaraja, S. S. Krishnan, and B. Gurumoorthy. "Onvex Partitioning Approach for the Construction of Solid Model From Measured Point Data." In ASME 1998 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/detc98/dac-5565.

Full text
Abstract:
Abstract This paper describes an algorithm for the construction of solid model from measured point data using Convex Partitioning approach. Convex Partitioning approach is based on the idea that any non-convex body can be viewed as a combination of several convex pieces. The input constitutes a set or cluster of points, measured on each face of the object, which is obtained by scanning the part. Points in each cluster are used to fit a plane or a non-planar surface depending upon the type of face. Partitioning is done along the planes till one gets all the convex pieces. The individual convex
APA, Harvard, Vancouver, ISO, and other styles
7

Hilasaca, Gladys M., and Fernando V. Paulovich. "A visual approach for user-guided feature fusion." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8313.

Full text
Abstract:
Dimensionality Reduction transforms data from high-dimensional space into visual space preserving the existing relationships. This abstract representation of complex data enables exploration of data similarities, but brings challenges about the analysis and interpretation for users on mismatching between their expectations and the visual representation. A possible way to model these understandings is via different feature extractors, because each feature has its own way to encode characteristics. Since there is no perfect feature extractor, the combination of multiple sets of features has been
APA, Harvard, Vancouver, ISO, and other styles
8

Gunel, B. T., E. Artun, S. Gul, B. Kulga, Y. D. Pak, and A. O. Herekeli. "Machine Learning-Based Estimation of Solids Content in Drilling Fluids Through Utilization of a Comprehensive Mud-Report Database." In SPE Europe Energy Conference and Exhibition. SPE, 2025. https://doi.org/10.2118/225536-ms.

Full text
Abstract:
Abstract Characterization and optimization of total solids content of drilling fluids is critical for the efficiency and success of drilling operations. Traditional solids content analysis methods, such as retort analysis, require substantial human intervention and time, which can lead to inaccuracies, time-management issues, and increased operational risks. To address these issues and complement automated rheological property measurements during drilling, this study aims to develop and validate a machine learning-based framework for estimating solids content in drilling fluids from readily av
APA, Harvard, Vancouver, ISO, and other styles
9

Fan, Xuhui, Bin Li, Ling Luo, and Scott A. Sisson. "Bayesian Nonparametric Space Partitions: A Survey." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/602.

Full text
Abstract:
Bayesian nonparametric space partition (BNSP) models provide a variety of strategies for partitioning a D-dimensional space into a set of blocks, such that the data within the same block share certain kinds of homogeneity. BNSP models are applicable to many areas, including regression/classification trees, random feature construction, and relational modelling. This survey provides the first comprehensive review of this subject. We explore the current progress of BNSP research through three perspectives: (1) Partition strategies, where we review the various techniques for generating partitions
APA, Harvard, Vancouver, ISO, and other styles
10

Supekar, N., S. Peddada, and J. Reid. "Analysis of Clustering Machine Learning Algorithms and Generative Artificial Intelligence Tool for Visualization and Interpretation of Seismic Data." In ADIPEC. SPE, 2024. http://dx.doi.org/10.2118/221999-ms.

Full text
Abstract:
Abstract Seismic attribute analysis is often confounded by the complexity of the subsurface. Clustering machine learning algorithms reduce complexity and bring the most salient features of the seismic datasets to the fore. By conducting a comparative study of clustering algorithms applied to 2D seismic data, we can determine a set of optimal methods for visualizing particular seismic attributes. This paper presents an analysis of clustering algorithms, and a generative artificial intelligence-based large language model (LLM) customized for interrogating seismic data. Based on data partitioning
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Feature set partitioning' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography