{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T23:41:22Z","timestamp":1764978082006,"version":"3.46.0"},"reference-count":29,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2018,10,22]],"date-time":"2018-10-22T00:00:00Z","timestamp":1540166400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The term \u201cbig data\u201d means a large amount of data, and big data management refers to the efficient handling, organization, or use of large volumes of structured and unstructured data belonging to an organization. Due to the gradual availability of plenty of raw data, the knowledge extraction process from big data is a very difficult task for most of the classical data mining and machine learning tools. In a previous paper, the correlative naive Bayes (CNB) classifier was developed for big data classification. This work incorporates the fuzzy theory along with the CNB classifier to develop the fuzzy CNB (FCNB) classifier. The proposed FCNB classifier solves the big data classification problem by using the MapReduce framework and thus achieves improved classification results. Initially, the database is converted to the probabilistic index table, in which data and attributes are presented in rows and columns, respectively. Then, the membership degree of the unique symbols present in each attribute of data is found. Finally, the proposed FCNB classifier finds the class of data based on training information. The simulation of the proposed FCNB classifier uses the localization and skin segmentation datasets for the purpose of experimentation. The results of the proposed FCNB classifier are analyzed based on the metrics, such as sensitivity, specificity, and accuracy, and compared with the various existing works.<\/jats:p>","DOI":"10.1515\/jisys-2018-0020","type":"journal-article","created":{"date-parts":[[2018,10,23]],"date-time":"2018-10-23T05:03:50Z","timestamp":1540271030000},"page":"994-1006","source":"Crossref","is-referenced-by-count":11,"title":["FCNB: Fuzzy Correlative Naive Bayes Classifier with MapReduce Framework for Big Data Classification"],"prefix":"10.1515","volume":"29","author":[{"given":"Chitrakant","family":"Banchhor","sequence":"first","affiliation":[{"name":"Research Scholar, Computer Science and Engineering Department , Koneru Lakshmaiah Education Foundation , Vaddeswaram, Guntur , India"}]},{"given":"N.","family":"Srinivasu","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department , Koneru Lakshmaiah Education Foundation , Vaddeswaram, Guntur , India"}]}],"member":"374","published-online":{"date-parts":[[2018,10,22]]},"reference":[{"key":"2025120523362781448_j_jisys-2018-0020_ref_001","doi-asserted-by":"crossref","unstructured":"\u00c1. Arnaiz-Gonz\u00e1lez, A. Gonz\u00e1lez-Rogel, J. F. D\u00edez-Pastor and C. L\u00f3pez-Nozal, MR-DIS: democratic instance selection for big data by MapReduce, Progr. Artif. Intell. 6 (2017), 211\u2013219.","DOI":"10.1007\/s13748-017-0117-5"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_002","doi-asserted-by":"crossref","unstructured":"C. Banchhor and N. Srinivasu, CNB-MRF: adapting correlative naive Bayes classifier and MapReduce framework for big data classification, Int. Rev. Comput. Softw. (IRECOS) 11 (2016).","DOI":"10.15866\/irecos.v11i11.10116"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_003","doi-asserted-by":"crossref","unstructured":"A. Bechini, F. Marcelloni and A. Segatori, A MapReduce solution for associative classification of big data, Inform. Sci. 332 (2016), 33\u201355.","DOI":"10.1016\/j.ins.2015.10.041"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_004","doi-asserted-by":"crossref","unstructured":"R. Bhukya and J. Gyani, Fuzzy associative classification algorithm based on MapReduce framework, in: Proceedings of the International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 357\u2013360, Davangere, 2015.","DOI":"10.1109\/ICATCCT.2015.7456909"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_005","doi-asserted-by":"crossref","unstructured":"J. Chen, H. Chen, X. Wan and G. Zheng, MR-ELM: a MapReduce-based framework for large-scale ELM training in big data era, Neural Comput. Appl. 27 (2016), 101\u2013110.","DOI":"10.1007\/s00521-014-1559-3"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_006","doi-asserted-by":"crossref","unstructured":"M. Duan, K. Li, X. Liao and K. Li, A parallel multiclassification algorithm for big data using an extreme learning machine, IEEE Trans. Neural Netw. Learn. Syst. 29 (2017), 2337\u20132351.","DOI":"10.1109\/TNNLS.2017.2654357"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_007","doi-asserted-by":"crossref","unstructured":"M. Elkano, M. Galar, J. Sanz and H. Bustince, CHI-BD: a fuzzy rule-based classification system for big data classification problems, Fuzzy Sets Syst. 348 (2018), 75\u2013101.","DOI":"10.1016\/j.fss.2017.07.003"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_008","doi-asserted-by":"crossref","unstructured":"A. Fern\u00e1ndez, S. del R\u00edo, A. Bawakid and F. Herrera, Fuzzy rule based classification systems for big data with MapReduce: granularity analysis, Adv. Data Anal. Classif. 11 (2017), 711\u2013730.","DOI":"10.1007\/s11634-016-0260-z"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_009","doi-asserted-by":"crossref","unstructured":"A. Haque, B. Parker, L. Khan and B. Thuraisingham, Evolving big data stream classification with MapReduce, in: Proceedings of IEEE 7th International Conference on Cloud Computing, pp. 570\u2013577, Anchorage, AK, 2014.","DOI":"10.1109\/CLOUD.2014.82"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_010","unstructured":"M. Hazewinkel, Arithmetic series, in: Encyclopedia of Mathematics, Springer, Netherlands, 2001."},{"key":"2025120523362781448_j_jisys-2018-0020_ref_011","doi-asserted-by":"crossref","unstructured":"O. Hegazy, S. Safwat and M. E. Bakry, A MapReduce fuzzy techniques of big data classification, in: Proceedings of the SAI Computing Conference (SAI), pp. 118\u2013128, London, 2016.","DOI":"10.1109\/SAI.2016.7555971"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_012","doi-asserted-by":"crossref","unstructured":"G. B. Huang, Q. Y. Zhu and C. K. Siew, Extreme learning machine: theory and applications, Neurocomputing 70 (2006), 489\u2013501.","DOI":"10.1016\/j.neucom.2005.12.126"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_013","doi-asserted-by":"crossref","unstructured":"X. Huang, L. Shi and J. A. K. Suykens, Support vector machine classifier with pinball loss, IEEE Trans. Pattern Anal. Mach. Intell. 36 (2014), 984\u2013997.","DOI":"10.1109\/TPAMI.2013.178"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_014","doi-asserted-by":"crossref","unstructured":"M. S. Kamal, S. Parvin, A. S. Ashour, F. Shi and N. Dey, De-Bruijn graph with MapReduce framework towards metagenomic data classification, Int. J. Inform. Technol. 9 (2017), 59\u201375.","DOI":"10.1007\/s41870-017-0005-z"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_015","doi-asserted-by":"crossref","unstructured":"W. Lin, Z. Wu, L. Lin, A. Wen and J. Li, An ensemble random forest algorithm for insurance big data analysis, IEEE Access 5 (2017), 16568\u201316575.","DOI":"10.1109\/ACCESS.2017.2738069"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_016","unstructured":"Localization dataset from https:\/\/archive.ics.uci.edu\/ml\/datasets\/Localization+Data+for+Person+Activity, Accessed on October 2017."},{"key":"2025120523362781448_j_jisys-2018-0020_ref_017","doi-asserted-by":"crossref","unstructured":"V. Lopez, S. del Rio, J. M. Benitez and F. Herrera, On the use of MapReduce to build linguistic fuzzy rule based classification systems for big data, in: Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1905\u20131912, Beijing, 2014.","DOI":"10.1109\/FUZZ-IEEE.2014.6891753"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_018","doi-asserted-by":"crossref","unstructured":"V. L\u00f3pez, S. del R\u00edo, J. M. Ben\u00edtez and F. Herrera, Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data, Fuzzy Sets Syst. 258 (2015), 5\u201338.","DOI":"10.1016\/j.fss.2014.01.015"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_019","doi-asserted-by":"crossref","unstructured":"S. A. Ludwig, MapReduce-based fuzzy c-means clustering algorithm: implementation and scalability, Int. J. Mach. Learn. Cybernet. 6 (2015), 923\u2013934.","DOI":"10.1007\/s13042-015-0367-0"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_020","doi-asserted-by":"crossref","unstructured":"J. Maillo, I. Triguero and F. Herrera, A MapReduce-based k-nearest neighbor approach for big data classification, in: IEEE Trustcom\/BigDataSE\/ISPA, pp. 167\u2013172, Helsinki, 2015.","DOI":"10.1109\/Trustcom.2015.577"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_021","doi-asserted-by":"crossref","unstructured":"S. Mirjalili, S. M. Mirjalili and A. Lewis, Grey wolf optimizer, Adv. Eng. Softw. 69 (2014), 46\u201361.","DOI":"10.1016\/j.advengsoft.2013.12.007"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_022","doi-asserted-by":"crossref","unstructured":"S. S. Patil and S. P. Sonavane, Enriched over_sampling techniques for improving classification of imbalanced big data, in: Proceedings of IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService), pp. 1\u201310, San Francisco, CA, 2017.","DOI":"10.1109\/BigDataService.2017.19"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_023","doi-asserted-by":"crossref","unstructured":"B. Pei, F. Wang and X. Wang, Research on MapReduce-based fuzzy associative classifier for big probabilistic numerical data, in: Proceedings of the IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 903\u2013906, Chengdu, 2016.","DOI":"10.1109\/iThings-GreenCom-CPSCom-SmartData.2016.186"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_024","doi-asserted-by":"crossref","unstructured":"G. Santafe, J. A. Lozano and P. Larranaga, Bayesian model averaging of naive Bayes for clustering, IEEE Trans. Syst. Man Cybernet. Pt. B (Cybernetics) 36 (2006), 1149\u20131161.","DOI":"10.1109\/TSMCB.2006.874132"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_025","doi-asserted-by":"crossref","unstructured":"A. Segatori, F. Marcelloni and W. Pedrycz, On distributed fuzzy decision trees for big data, IEEE Trans. Fuzzy Syst. 26 (2018), 174\u2013192.","DOI":"10.1109\/TFUZZ.2016.2646746"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_026","unstructured":"Skin segmentation dataset from https:\/\/archive.ics.uci.edu\/ml\/datasets\/skin+segmentation, Accessed on October 2017."},{"key":"2025120523362781448_j_jisys-2018-0020_ref_027","unstructured":"H. Storr, A compact fuzzy extension of the naive Bayesian classification algorithm, in: Intelligent Systems in e-Commerce (ISeC), 2002."},{"key":"2025120523362781448_j_jisys-2018-0020_ref_028","doi-asserted-by":"crossref","unstructured":"I. Triguero, D. Peralta, J. Bacardit, S. Garc\u00eda and F. Herrera, MRPR: a MapReduce solution for prototype reduction in big data classification, Neurocomputing 150 (2015), 331\u2013345.","DOI":"10.1016\/j.neucom.2014.04.078"},{"key":"2025120523362781448_j_jisys-2018-0020_ref_029","doi-asserted-by":"crossref","unstructured":"J. Zhai, S. Zhang and C. Wang, The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers, Int. J. Mach. Learn. Cybernet. 8 (2017), 1009\u20131017.","DOI":"10.1007\/s13042-015-0478-7"}],"container-title":["Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/view\/journals\/jisys\/29\/1\/article-p994.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2018-0020\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2018-0020\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T23:38:54Z","timestamp":1764977934000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2018-0020\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,22]]},"references-count":29,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,4,25]]},"published-print":{"date-parts":[[2019,12,18]]}},"alternative-id":["10.1515\/jisys-2018-0020"],"URL":"https:\/\/doi.org\/10.1515\/jisys-2018-0020","relation":{},"ISSN":["2191-026X","0334-1860"],"issn-type":[{"type":"electronic","value":"2191-026X"},{"type":"print","value":"0334-1860"}],"subject":[],"published":{"date-parts":[[2018,10,22]]}}}