{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T23:30:49Z","timestamp":1764977449473,"version":"3.46.0"},"reference-count":27,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2016,11,26]],"date-time":"2016-11-26T00:00:00Z","timestamp":1480118400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,3,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Recently, classification systems have received significant attention among researchers due to the important characteristics and behaviors of analysis required in real-time databases. Among the various classification-based methods suitable for real-time databases, fuzzy rule-based classification is effectively used by different researchers in various fields. An important issue in the design of fuzzy rule-based classification is the automatic generation of fuzzy if-then rules and the membership functions. The literature presents different techniques for automatic fuzzy design. Among the different techniques available in the literature, choosing the type, the number of membership functions, and defining parameters of membership function are still challenging tasks. In order to handle these challenges in the fuzzy rule-based classification system, this paper proposes a brain genetic fuzzy system (BGFS) for data classification by newly devising the exponential genetic brain storm optimization. Here, membership functions are optimally devised using exponential genetic brain storm optimization algorithm and rules are derived using the exponential brain storm optimization algorithm. The designed membership function and fuzzy rules are then effectively utilized for data classification. The proposed BGFS is analyzed with four datasets, using sensitivity, specificity, and accuracy. The outcome ensures that the proposed BGFS obtained the maximum accuracy of 88.8%, which is high as compared with the existing adaptive genetic fuzzy system.<\/jats:p>","DOI":"10.1515\/jisys-2016-0034","type":"journal-article","created":{"date-parts":[[2016,11,26]],"date-time":"2016-11-26T05:01:29Z","timestamp":1480136489000},"page":"231-247","source":"Crossref","is-referenced-by-count":6,"title":["BGFS: Design and Development of Brain Genetic Fuzzy System for Data Classification"],"prefix":"10.1515","volume":"27","author":[{"given":"Chandrasekar","family":"Ravi","sequence":"first","affiliation":[{"name":"School of Information Technology and Engineering , VIT University , Vellore, Tamil Nadu 632014 , India"}]},{"given":"Neelu","family":"Khare","sequence":"additional","affiliation":[{"name":"School of Information Technology and Engineering , VIT University , Vellore, Tamil Nadu 632014 , India"}]}],"member":"374","published-online":{"date-parts":[[2016,11,26]]},"reference":[{"key":"2025120523273717596_j_jisys-2016-0034_ref_001_w2aab3b7b2b1b6b1ab1b6b1Aa","doi-asserted-by":"crossref","unstructured":"P. P. Angelov and R. A. Buswell, Automatic generation of fuzzy rule-based models from data by genetic algorithms, Inform. Sci.150 (2003), 17\u201331.10.1016\/S0020-0255(02)00367-5","DOI":"10.1016\/S0020-0255(02)00367-5"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_002_w2aab3b7b2b1b6b1ab1b6b2Aa","doi-asserted-by":"crossref","unstructured":"D. Binu and M. Selvi, BFC: bat algorithm based fuzzy classifier for medical data classification, J. Med. Imaging Health Inform.5 (2015), 599\u2013606.10.1166\/jmihi.2015.1428","DOI":"10.1166\/jmihi.2015.1428"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_003_w2aab3b7b2b1b6b1ab1b6b3Aa","doi-asserted-by":"crossref","unstructured":"J. Casillas, B. Carse and L. Bull, Fuzzy-XCS: a Michigan genetic fuzzy system, IEEE Trans. Fuzzy Syst.15 (2007), 536\u2013550.10.1109\/TFUZZ.2007.900904","DOI":"10.1109\/TFUZZ.2007.900904"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_004_w2aab3b7b2b1b6b1ab1b6b4Aa","doi-asserted-by":"crossref","unstructured":"W. Combs and J. Andrews, Combinatorial rule explosion eliminated by a fuzzy rule configuration, IEEE Trans. Fuzzy Syst.6 (1998), 1\u201311.10.1109\/91.660804","DOI":"10.1109\/91.660804"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_005_w2aab3b7b2b1b6b1ab1b6b5Aa","doi-asserted-by":"crossref","unstructured":"B. Dennis and S. Muthukrishnan, AGFS: adaptive genetic fuzzy system for medical data classification, Appl. Soft Comput.25 (2014), 242\u2013252.10.1016\/j.asoc.2014.09.032","DOI":"10.1016\/j.asoc.2014.09.032"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_006_w2aab3b7b2b1b6b1ab1b6b6Aa","doi-asserted-by":"crossref","unstructured":"P. GaneshKumar, C. Rani, D. Devaraj and T. A. A. Victoire, Hybrid ant bee algorithm for fuzzy expert system based sample classification, IEEE\/ACM Trans. Comput. Biol. Bioinform.11 (2014), 347\u2013360.10.1109\/TCBB.2014.2307325","DOI":"10.1109\/TCBB.2014.2307325"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_007_w2aab3b7b2b1b6b1ab1b6b7Aa","doi-asserted-by":"crossref","unstructured":"P. GaneshKumar, T. A. A. Victoire, P. Renukadevi and D. Devaraj, Design of fuzzy expert system for microarray data classification using a novel Genetic Swarm Algorithm, Expert Syst. Appl.39 (2012), 1811\u20131821.10.1016\/j.eswa.2011.08.069","DOI":"10.1016\/j.eswa.2011.08.069"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_008_w2aab3b7b2b1b6b1ab1b6b8Aa","doi-asserted-by":"crossref","unstructured":"A. Goel and S. Kr. Srivastava, Role of kernel parameters in performance evaluation of SVM, in: Proceedings of 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT), February 2016.","DOI":"10.1109\/CICT.2016.40"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_009_w2aab3b7b2b1b6b1ab1b6b9Aa","doi-asserted-by":"crossref","unstructured":"M. A. Grahama, A. Mukherjee and S. Chakraborti, Distribution-free exponentially weighted moving average control charts for monitoring unknown location, Comput. Stat. Data Anal.56 (2012), 2539\u20132561.10.1016\/j.csda.2012.02.010","DOI":"10.1016\/j.csda.2012.02.010"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_010_w2aab3b7b2b1b6b1ab1b6c10Aa","doi-asserted-by":"crossref","unstructured":"Y. Hayashi and S. Yukita, Rule extraction using Recursive-Rule extraction algorithm with J48graft combined with sampling selection techniques for the diagnosis of type 2 diabetes mellitus in the Pima Indian dataset, Inform. Med. Unlocked2 (2016), 92\u2013104.10.1016\/j.imu.2016.02.001","DOI":"10.1016\/j.imu.2016.02.001"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_011_w2aab3b7b2b1b6b1ab1b6c11Aa","doi-asserted-by":"crossref","unstructured":"F. Herrera, Genetic fuzzy systems: taxonomy, current research trends and prospects, Evol. Intell.1 (2008), 27\u201346.10.1007\/s12065-007-0001-5","DOI":"10.1007\/s12065-007-0001-5"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_012_w2aab3b7b2b1b6b1ab1b6c12Aa","doi-asserted-by":"crossref","unstructured":"Y. Hu, R. Chen and G. Tzeng, Finding fuzzy classification rules using data mining techniques, Pattern Recognit. Lett.24 (2003), 509\u2013519.10.1016\/S0167-8655(02)00273-8","DOI":"10.1016\/S0167-8655(02)00273-8"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_013_w2aab3b7b2b1b6b1ab1b6c13Aa","doi-asserted-by":"crossref","unstructured":"I. Jagielska, C. Matthews and T. Whitfort, An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems, Neurocomputing24 (1999), 37\u201354.10.1016\/S0925-2312(98)00090-3","DOI":"10.1016\/S0925-2312(98)00090-3"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_014_w2aab3b7b2b1b6b1ab1b6c14Aa","doi-asserted-by":"crossref","unstructured":"Y. Jin, Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement, IEEE Trans. Fuzzy Syst.8 (2000), 212\u2013221.10.1109\/91.842154","DOI":"10.1109\/91.842154"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_015_w2aab3b7b2b1b6b1ab1b6c15Aa","doi-asserted-by":"crossref","unstructured":"C. F. Juang, C. W. Hung and C. H. Hsu, Rule-based cooperative continuous ant colony optimization to improve the accuracy of fuzzy system design, IEEE Trans. Fuzzy Syst.22 (2014), 723\u2013735.10.1109\/TFUZZ.2013.2272480","DOI":"10.1109\/TFUZZ.2013.2272480"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_016_w2aab3b7b2b1b6b1ab1b6c16Aa","doi-asserted-by":"crossref","unstructured":"H. Liu, A. Gegov and M. Cocea, Complexity control in rule based models for classification in machine learning context, Adv. Comput. Intell. Syst.513 (2016), 125\u2013143.","DOI":"10.1007\/978-3-319-46562-3_9"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_017_w2aab3b7b2b1b6b1ab1b6c17Aa","doi-asserted-by":"crossref","unstructured":"D. Meng and Z. Pei, Extracting linguistic rules from data sets using fuzzy logic and genetic algorithms, Neurocomputing78 (2012), 48\u201354.10.1016\/j.neucom.2011.05.029","DOI":"10.1016\/j.neucom.2011.05.029"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_018_w2aab3b7b2b1b6b1ab1b6c18Aa","unstructured":"M. Mitchell, An introduction to genetic algorithms, MIT Press, Cambridge, MA, 1998."},{"key":"2025120523273717596_j_jisys-2016-0034_ref_019_w2aab3b7b2b1b6b1ab1b6c19Aa","doi-asserted-by":"crossref","unstructured":"Purushottam, K. Saxena and R. Sharma, Efficient heart disease prediction system using decision tree, in: Proceedings of 2015 International Conference on Computing, Communication & Automation (ICCCA), May 2015.","DOI":"10.1109\/CCAA.2015.7148346"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_020_w2aab3b7b2b1b6b1ab1b6c20Aa","unstructured":"C. Ravi and N. Khare, BSFS: design and development of exponential brain storm fuzzy system for data classification, Int. J. Uncertain. Fuzziness Knowl. Based Syst., accepted."},{"key":"2025120523273717596_j_jisys-2016-0034_ref_021_w2aab3b7b2b1b6b1ab1b6c21Aa","doi-asserted-by":"crossref","unstructured":"C. A. P. Reyes, Evolutionary fuzzy modeling, in: Coevolutionary Fuzzy Modeling, Lecture Notes in Computer Science, 3204, pp. 27\u201350, 2004.","DOI":"10.1007\/978-3-540-30118-9_2"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_022_w2aab3b7b2b1b6b1ab1b6c22Aa","doi-asserted-by":"crossref","unstructured":"M. Russo, FuGeNeSys \u2013 a fuzzy genetic neural system for fuzzy modeling, IEEE Trans. Fuzzy Syst.6 (1998), 373\u2013388.10.1109\/91.705506","DOI":"10.1109\/91.705506"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_023_w2aab3b7b2b1b6b1ab1b6c23Aa","doi-asserted-by":"crossref","unstructured":"G. Schaefer and T. Nakashima, Data mining of gene expression data by fuzzy and hybrid fuzzy methods, IEEE Trans. Inform. Technol. Biomed.14 (2010), 23\u201329.10.1109\/TITB.2009.2033590","DOI":"10.1109\/TITB.2009.2033590"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_024_w2aab3b7b2b1b6b1ab1b6c24Aa","doi-asserted-by":"crossref","unstructured":"Y. Shi, Brain storm optimization algorithm, in: Advances in Swarm Intelligence, LNCS, 6728, pp. 303\u2013309, 2011.","DOI":"10.1007\/978-3-642-21515-5_36"},{"key":"2025120523273717596_j_jisys-2016-0034_ref_025_w2aab3b7b2b1b6b1ab1b6c25Aa","unstructured":"UCI Machine Learning Repository, Available from http:\/\/archive.ics.uci.edu\/ml\/, Accessed 10 October, 2015."},{"key":"2025120523273717596_j_jisys-2016-0034_ref_026_w2aab3b7b2b1b6b1ab1b6c26Aa","unstructured":"X. S. Yang, Nature-Inspired Metaheuristic Algorithms, Luniver Press, 2008."},{"key":"2025120523273717596_j_jisys-2016-0034_ref_027_w2aab3b7b2b1b6b1ab1b6c27Aa","unstructured":"Y. Yi and E. Hullermeier, Learning complexity-bounded rule-based classifiers by combining association analysis and genetic algorithms, in: Proceedings of the Joint 4th Conference of the European Society for Fuzzy Logic and Technology, pp. 47\u201352, Barcelona, Spain, September 2005."}],"container-title":["Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.degruyter.com\/view\/j\/jisys.2018.27.issue-2\/jisys-2016-0034\/jisys-2016-0034.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2016-0034\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2016-0034\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T23:27:58Z","timestamp":1764977278000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2016-0034\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,26]]},"references-count":27,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2016,9,14]]},"published-print":{"date-parts":[[2018,3,28]]}},"alternative-id":["10.1515\/jisys-2016-0034"],"URL":"https:\/\/doi.org\/10.1515\/jisys-2016-0034","relation":{},"ISSN":["2191-026X","0334-1860"],"issn-type":[{"type":"electronic","value":"2191-026X"},{"type":"print","value":"0334-1860"}],"subject":[],"published":{"date-parts":[[2016,11,26]]}}}