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Journal articles on the topic 'Data Classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Sutanapong., Chanoknath, and P. Louangrath. "Data Classification and Distribution." Inter. J. Res. Methodol. Soc. Sci 1, no. 2 (2015): 36–47. https://doi.org/10.5281/zenodo.1320784.

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The objective of this paper is to explain the four main types of data. The classification of data by type is important for statistical analysis. In particular, data classification is useful for quantitative research in social science. Data are defined as a quantitative measurement of qualitative fact. Data are classified into three types: quantitative, ordinal and nominal. Quantitative data are those that may be subject to mathematical operations: addition, subtraction, multiplication and division. Ordinal data are those that rank the values in a data set in an ascending order (from low to hig

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Smita, Nirkhi, and Patil Shashikant. "Comprehensive Assessment of Imbalanced Data Classification." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 1426–31. https://doi.org/10.35940/ijeat.D7349.049420.

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This is an attempt to address the various challenges opportunities and scope for formulating and designing new procedure in imbalanced classification problem which poses a challenge to a predictive modelling as many of AI ML n DL algorithms which are extensively used for classification are always designed from the perspective of with majority of focus on assuming equal number of examples for a class. It leads to poor efficiency and performance especially in minority class. As Minority class is always very crucial and sensitive to classification errors and also its utmost important in imbalance

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Geethika, Paruchuri, and Voleti Prasanthi. "Booster in High Dimensional Data Classification." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 1186–90. http://dx.doi.org/10.31142/ijtsrd11368.

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Alhaisoni, Majed Mohaia, Rabie A. Ramadan, and Ahmed Y. Khedr. "SCF: Smart Big Data Classification Framework." Indian Journal of Science and Technology 12, no. 37 (2019): 1–8. http://dx.doi.org/10.17485/ijst/2019/v12i37/148647.

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5

S, Gowtham, and Karuppusamy S. "Review of Data Mining Classification Techniques." Bonfring International Journal of Software Engineering and Soft Computing 9, no. 2 (2019): 8–11. http://dx.doi.org/10.9756/bijsesc.9013.

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6

Uprichard, Emma. "Dirty Data: Longitudinal Classification Systems." Sociological Review 59, no. 2_suppl (2011): 93–112. http://dx.doi.org/10.1111/j.1467-954x.2012.02058.x.

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Typically in longitudinal quantitative research, classifications are tracked over time. However, most classifications change in absolute terms in that some die whilst others are created, and in their meaning. There is a need, therefore, to re-think how longitudinal quantitative research might explore both the qualitative changes to classification systems as well as the quantitative changes within each classification. By drawing on the changing classifications of local food retail outlets in the city of York (UK) since the 1950s as an illustrative example, an alternative way of graphing longitu

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Anam, Mamoona, Dr Kantilal P. Rane, Ali Alenezi, Ruby Mishra, Dr Swaminathan Ramamurthy, and Ferdin Joe John Joseph. "Content Classification Tasks with Data Preprocessing Manifestations." Webology 19, no. 1 (2022): 1413–30. http://dx.doi.org/10.14704/web/v19i1/web19094.

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Deep reinforcement learning has a major hurdle in terms of data efficiency. We solve this challenge by pretraining an encoder with unlabeled input, which is subsequently finetuned on a tiny quantity of task-specific input. We use a mixture of latent dynamics modelling and unsupervised goal-conditioned RL to encourage learning representations that capture various elements of the underlying MDP. Our approach significantly outperforms previous work combining offline representation pretraining with task-specific finetuning when limited to 100k steps of interaction on Atari games (equivalent to two

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Rani, A. Nithya, and Dr Antony Selvdoss Davamani. "Classification on Missing Data for Multiple Imputations." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 745–49. http://dx.doi.org/10.31142/ijtsrd9566.

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Gniazdowski, Zenon, and Michał Grabowski. "Numerical Coding of Nominal Data." Zeszyty Naukowe WWSI 9, no. 12 (2015): 53——61. https://doi.org/10.26348/znwwsi.12.53.

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In this paper, a novel approach for coding nominal data is proposed. For the given nominal data, a rank in a form of complex number is assigned. The proposed method does not lose any information about the attribute and brings other properties previously unknown. The approach based on these knew properties can been used for classification. The analyzed example shows that classification with the use of coded nominal data or both numerical as well as coded nominal data is more effective than the classification, which uses only numerical data.

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N.J., Anjala. "Algorithmic Assessment of Text based Data Classification in Big Data Sets." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 1231–34. http://dx.doi.org/10.5373/jardcs/v12sp4/20201598.

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Bian, Jiang, Dayong Tian, Yuanyan Tang, and Dacheng Tao. "Trajectory Data Classification." ACM Transactions on Intelligent Systems and Technology 10, no. 4 (2019): 1–34. http://dx.doi.org/10.1145/3330138.

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12

Suthaharan, Shan. "Big data classification." ACM SIGMETRICS Performance Evaluation Review 41, no. 4 (2014): 70–73. http://dx.doi.org/10.1145/2627534.2627557.

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Anonymous. "Sonar data classification." Eos, Transactions American Geophysical Union 69, no. 38 (1988): 868. http://dx.doi.org/10.1029/88eo01128.

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14

Parashar, Komal. "Telugu Data Classification." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 2852–57. http://dx.doi.org/10.22214/ijraset.2024.59508.

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Abstract: Telugu is considered as the difficult languages which is morphologically rich when it comes to Dravidian languages. There are many Telugu documents available on Internet, it is important to organize the data by automatically by assigning a collection of text with predefined categories. Here the Telugu data is classified into multiple areas like business, sports, entertainment, nation, editorial is the main goal throughout this research work. This research work provides up an efficient model by adopting some ML classifiers such as SVM, Naive Bayes, and Logistic regression to perform s

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G., Dr Ayyappan. "Various classifications for caesarian section classification dataset data set." Indian Journal of Computer Science and Engineering 9, no. 6 (2018): 145–47. http://dx.doi.org/10.21817/indjcse/2018/v9i6/180906013.

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Ustuner, M., F. B. Sanli, and S. Abdikan. "BALANCED VS IMBALANCED TRAINING DATA: CLASSIFYING RAPIDEYE DATA WITH SUPPORT VECTOR MACHINES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 379–84. http://dx.doi.org/10.5194/isprs-archives-xli-b7-379-2016.

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The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in

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Ustuner, M., F. B. Sanli, and S. Abdikan. "BALANCED VS IMBALANCED TRAINING DATA: CLASSIFYING RAPIDEYE DATA WITH SUPPORT VECTOR MACHINES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 379–84. http://dx.doi.org/10.5194/isprsarchives-xli-b7-379-2016.

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The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in

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18

Zhang, Lianjun, Chuangmin Liu, Craig J. Davis, Dale S. Solomon, Thomas B. Brann, and Lawrence E. Caldwell. "Fuzzy Classification of Ecological Habitats from FIA Data." Forest Science 50, no. 1 (2004): 117–27. http://dx.doi.org/10.1093/forestscience/50.1.117.

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Abstract Fuzzy c-mean classification (FCM), multi-layer perceptron neural network (MLP), fuzzy ARTMAP, and linear discriminant analysis (LDA) were used to classify Forest Inventory and Analysis (FIA) plots into six ecological habitats in the U.S. Northeast. Among the four classifiers, both FCM and MLP produced “soft” classifications based on fuzzy membership values. In contrast, fuzzy ARTMAP and LDA generated only “hard” classifications in which a plot is assigned to the dominant habitat class, based on binary logic, without considering any coexisting classes. The error matrix and several accu

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Adillah, Dayana Ahmad Dali, Aswa Omar Nurul, and Mustapha Aida. "Data Mining Approach to Herbs Classification." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 2 (2018): 570–76. https://doi.org/10.11591/ijeecs.v12.i2.pp570-576.

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Herbs are one of the high-value products in Malaysia. The term &bdquo;herbs" has more than one definition. It is also demanding by multiple manifolds. Herbs are used in many sectors nowadays. The ability to identify variety herbs in the market is quite hard without the intervention of human experts. Unfortunately, human experts are prone to error. Herbs classification is able to assist human experts and at the same time minimizing the intervention. This research performs identification and classification of herbs based on image capture ad variety of classification algorithms such as an Artific

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R, Vijaya Kumar Reddy, Srinivasa Rao B, Shaik Subhani, and Ravi Prakash. "Effectiveness of Data Augmentation on Handwritten Digit Classification." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12 (2019): 90–96. http://dx.doi.org/10.5373/jardcs/v11i12/20193216.

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Divya, G., and V. Maniraj. "Optimizing Supervised Classification Algorithms for Real - World Data." International Journal of Science and Research (IJSR) 14, no. 3 (2025): 289–96. https://doi.org/10.21275/sr25305104046.

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22

Reddy Polu, Omkar. "AI-Driven Sleep Disorder Classification Using EEG Data." International Journal of Science and Research (IJSR) 13, no. 1 (2024): 1844–49. https://doi.org/10.21275/sr24011114422.

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23

Aydadenta, Husna, and Adiwijaya. "On the classification techniques in data mining for microarray data classification." Journal of Physics: Conference Series 971 (March 2018): 012004. http://dx.doi.org/10.1088/1742-6596/971/1/012004.

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24

Otley, Amanda, Michelle Morris, Andy Newing, and Mark Birkin. "Local and Application-Specific Geodemographics for Data-Led Urban Decision Making." Sustainability 13, no. 9 (2021): 4873. http://dx.doi.org/10.3390/su13094873.

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This work seeks to introduce improvements to the traditional variable selection procedures employed in the development of geodemographic classifications. It presents a proposal for shifting from a traditional approach for generating general-purpose one-size-fits-all geodemographic classifications to application-specific classifications. This proposal addresses the recent scepticism towards the utility of general-purpose applications by employing supervised machine learning techniques in order to identify contextually relevant input variables from which to develop geodemographic classifications

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25

Yin, Yin Cho, Htay Khin, and Myat Thuzar Win. "Hospital Medicine Classification using Data Mining Techniques." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1607–9. https://doi.org/10.5281/zenodo.3591150.

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Data mining is a process which finds useful patterns from large amount of data. This paper is analyzing decision trees technique using the medicine data from central medical store, department of health. The results are support to best solution to share many type of medicine for manager at Central Medical store. This Central Medical store is supporting medicine to hospitals from upper Myanmar. Yin Yin Cho | Khin Htay | Win Myat Thuzar "Hospital Medicine Classification using Data Mining Techniques" Published in International Journal of Trend in Scientific Research and Development (ijts

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Ambulkar, Bhagyashree, and Prof Gunjan Agre. "Data Mining Over Encrypted Data of Database Client Engine Using Hybrid Classification Approach." International Journal of Innovative Research in Computer Science & Technology 5, no. 3 (2017): 291–94. http://dx.doi.org/10.21276/ijircst.2017.5.3.7.

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27

Patil, Swati B., and Arjun Kuruva. "Analysis of User Session Data using the Map Reduce Classification with Big Data." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (2018): 2008–11. http://dx.doi.org/10.31142/ijtsrd18221.

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28

Sikun, Gan. "BIG DATA EMOTION CLASSIFICATION." Young Scholars Journal, no. 1-2 (2022): 18–22. http://dx.doi.org/10.29013/ysj-22-1.2-18-22.

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29

Belarouci, Sara, and Mohammed Amine Chikh. "Medical imbalanced data classification." Advances in Science, Technology and Engineering Systems Journal 2, no. 3 (2017): 116–24. http://dx.doi.org/10.25046/aj020316.

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30

Picka, Jeffrey D. "Data Science and Classification." Technometrics 49, no. 3 (2007): 363–64. http://dx.doi.org/10.1198/tech.2007.s513.

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31

PENG, L., B. YANG, Y. CHEN, and A. ABRAHAM. "Data gravitation based classification." Information Sciences 179, no. 6 (2009): 809–19. http://dx.doi.org/10.1016/j.ins.2008.11.007.

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32

Borodko, A. "CLASSIFICATION OF DATA CENTERS." Telecom IT 7, no. 1 (2019): 1–9. http://dx.doi.org/10.31854/2307-1303-2019-7-1-1-9.

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The data center (DC) is the most progressive form of computing resources when it is necessary to provide services to a wide range of users. Research subject. The article discusses the classification of data centers, their main functions, composition, purpose of creation and factors affecting them. Methodology and core results. The article provides a classification and structural analysis of the methods and technologies for constructing information storage and processing systems. In the work with a systematic approach analyzed factors affecting data centers. Practical relevance. It consists in

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Raxmanov, Asqar Тajibaevich, and Zebiniso Abdulxamidovna Abduvaliyeva. "CLASSIFICATION OF TEXT DATA." INTERNATIONAL SCIENTIFIC-ELECTRONIC JOURNAL "PIONEERING STUDIES AND THEORIES" 1, no. 4 (2025): 12–16. https://doi.org/10.5281/zenodo.15046299.

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This article discusses the TF-IDF modeling method for converting given text data into a vector quantity for further classification. Using the TF-IDF modeling method, a cosine classifier for text data is obtained. This classifier can be used not only for classifying text data but also for classifying arbitrary vector data. The use of the obtained modeling and classification results with other methods allows determining the most effective classification method. 

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Jiang, Tingxuan, Harald van der Werff, and Freek van der Meer. "Classification Endmember Selection with Multi-Temporal Hyperspectral Data." Remote Sensing 12, no. 10 (2020): 1575. http://dx.doi.org/10.3390/rs12101575.

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In hyperspectral image classification, so-called spectral endmembers are used as reference data. These endmembers are either extracted from an image or taken from another source. Research has shown that endmembers extracted from an image usually perform best when classifying a single image. However, it is unclear if this also holds when classifying multi-temporal hyperspectral datasets. In this paper, we use spectral angle mapper, which is a frequently used classifier for hyperspectral datasets to classify multi-temporal airborne visible/infrared imaging spectrometer (AVIRIS) hyperspectral ima

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Kumari, S. Surya, and G. Anjan Babu. "Sentiment classification using unlabelled data with emoticon classification." International Journal of Knowledge Engineering and Soft Data Paradigms 7, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijkesdp.2020.112616.

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Kumari, S. Surya, and G. Anjan Babu. "Sentiment classification using unlabelled data with emoticon classification." International Journal of Knowledge Engineering and Soft Data Paradigms 7, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijkesdp.2020.10034771.

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37

Senthamil Selvi, M., and S. Jansi Rani. "Classification of Admission Data Using Classification Learner Toolbox." Journal of Physics: Conference Series 1979, no. 1 (2021): 012043. http://dx.doi.org/10.1088/1742-6596/1979/1/012043.

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Myat, Mon Kyaw, San Nwe San, and Myint Yee Myint. "Pest Classification and Pesticide Recommendation System." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 2187–91. https://doi.org/10.5281/zenodo.3591203.

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Myanmar is an agricultural country and agriculture constitutes the largest sector of the economy. Recognizing of pests is a vital problem especially for farmers, agricultural researchers, and environmentalists. The proposed system is to classify the types of pest using the CNN model, which is often used when applying deep learning to image processing, and to recommend the most suitable pesticide according to the type of pest. This system will help to know easily information of pests and pesticides which should be used to the user. Using a public dataset of 1265 images of pests, a convolutional

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McDonough, Caitrin W., Steven M. Smith, Rhonda M. Cooper-DeHoff, and William R. Hogan. "Optimizing Antihypertensive Medication Classification in Electronic Health Record-Based Data: Classification System Development and Methodological Comparison." JMIR Medical Informatics 8, no. 2 (2020): e14777. http://dx.doi.org/10.2196/14777.

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Background Computable phenotypes have the ability to utilize data within the electronic health record (EHR) to identify patients with certain characteristics. Many computable phenotypes rely on multiple types of data within the EHR including prescription drug information. Hypertension (HTN)-related computable phenotypes are particularly dependent on the correct classification of antihypertensive prescription drug information, as well as corresponding diagnoses and blood pressure information. Objective This study aimed to create an antihypertensive drug classification system to be utilized with

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40

Krawczyk, Bartosz, Jerzy Stefanowski, and Michał Wozniak. "Data stream classification and big data analytics." Neurocomputing 150 (February 2015): 238–39. http://dx.doi.org/10.1016/j.neucom.2014.10.025.

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41

Mirarchi, Domenico, Giovanni Canino, Patrizia Vizza, et al. "Data mining techniques for vestibular data classification." International Journal of Internet Technology and Secured Transactions 7, no. 1 (2017): 51. http://dx.doi.org/10.1504/ijitst.2017.085734.

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Petrolo, Claudio, Salvatore Cuomo, Pierangelo Veltri, et al. "Data mining techniques for vestibular data classification." International Journal of Internet Technology and Secured Transactions 7, no. 1 (2017): 51. http://dx.doi.org/10.1504/ijitst.2017.10006656.

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Ytow, Nozomi. "Taxonaut: an application software for comparative display of multiple taxonomies with a use case of GBIF Species API." Biodiversity Data Journal 4 (September 30, 2016): e9787. https://doi.org/10.3897/BDJ.4.e9787.

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The Species API of the Global Biodiversity Information Facility (GBIF) provides public access to taxonomic data aggregated from multiple data sources. Each data source follows its own classification which can be inconsistent with classifications from other sources. Even with a reference classification e.g. the GBIF Backbone taxonomy, a comprehensive method to compare classifications in the data aggregation is essential, especially for non-expert users. A Java application was developed to compare multiple taxonomies graphically using classification data acquired from GBIF's ChecklistBank via th

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Divya, Agrawal* Padma Bonde. "PERFORMANCE ENHANCEMENT OF CLASSIFICATION ACCURACY BY ENSEMBLING TECHNIQUE USING IMBALANCED DATA." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 12 (2016): 170–76. https://doi.org/10.5281/zenodo.192547.

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Classification is one of the critical task in datamining. Many classifiers exist for classification task and each have their own pros and cons. It is observed that due to imbalancing in datasets quality of classification accuracy is decreasing. Thus the increasing rate of data diversity and size decreases the performance and efficiency of classifiers. Thus it is very much important to get the maximum classification accuracy. Ensemble learning is a simple, useful and effective meta-classification methodology that combines the predictions from various classifiers. In this research an empirical s

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Willatt, D. J., M. S. McCormick, R. P. Morton, and P. M. Stell. "Staging of Maxillary Cancer." Annals of Otology, Rhinology & Laryngology 96, no. 2 (1987): 137–41. http://dx.doi.org/10.1177/000348948709600201.

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Of the many proposed classifications for staging maxillary sinus cancer, none has been adopted universally and none is known to be superior to the others. This study identified the best of six currently used classifications using data from 53 previously untreated patients with squamous cell carcinoma of the maxillary sinus. Analysis of each classification's ability to stage the majority of patients, produce a balanced distribution of T stages, and correlate T stage with treatment and prognosis revealed Harrison's classification to be the best. Harrison's classification should be adopted worldw

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Di Prinzio, M., A. Castellarin, and E. Toth. "Data-driven catchment classification: application to the pub problem." Hydrology and Earth System Sciences 15, no. 6 (2011): 1921–35. http://dx.doi.org/10.5194/hess-15-1921-2011.

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Abstract. A promising approach to catchment classification makes use of unsupervised neural networks (Self Organising Maps, SOM's), which organise input data through non-linear techniques depending on the intrinsic similarity of the data themselves. Our study considers ∼300 Italian catchments scattered nationwide, for which several descriptors of the streamflow regime and geomorphoclimatic characteristics are available. We compare a reference classification, identified by using indices of the streamflow regime as input to SOM, with four alternative classifications, which were identified on the

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Yastikli, N., and Z. Cetin. "CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 441–45. http://dx.doi.org/10.5194/isprs-archives-xli-b3-441-2016.

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LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recen

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Chincholkar, Bhushan R. "Implementation Analysis of Data Classification Approach for Sentiment Classification." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1509–12. http://dx.doi.org/10.22214/ijraset.2021.36613.

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Sentiment analysis is one of the fastest growing fields with its demand and potential benefits that are increasing every day. Sentiment analysis aims to classify the polarity of a document through natural language processing, text analysis. With the help of internet and modern technology, there has bee n a tremendous growth in the amount of data. Each individual is in position to precise his/her own ideas freely on social media. All of this data can be analyzed and used in order to draw benefits and quality information. In this paper, the focus is on cyber-hate classification based on for publ

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Yastikli, N., and Z. Cetin. "CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 441–45. http://dx.doi.org/10.5194/isprsarchives-xli-b3-441-2016.

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LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recen

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50

Kim, Yeseul, Kyung-Do Lee, Sang-Il Na, Suk-Young Hong, No-Wook Park, and Hee Young Yoo. "MODIS Data-based Crop Classification using Selective Hierarchical Classification." Korean Journal of Remote Sensing 32, no. 3 (2016): 235–44. http://dx.doi.org/10.7780/kjrs.2016.32.3.3.

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