{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T10:17:58Z","timestamp":1779358678310,"version":"3.51.4"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2018,6,2]],"date-time":"2018-06-02T00:00:00Z","timestamp":1527897600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2018,11]]},"DOI":"10.1007\/s10489-018-1204-4","type":"journal-article","created":{"date-parts":[[2018,6,2]],"date-time":"2018-06-02T02:32:28Z","timestamp":1527906748000},"page":"4212-4231","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Entropy based fuzzy least squares twin support vector machine for class imbalance learning"],"prefix":"10.1007","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6375-8615","authenticated-orcid":false,"given":"Deepak","family":"Gupta","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bharat","family":"Richhariya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,6,2]]},"reference":[{"issue":"1","key":"1204_CR1","first-page":"2472","volume":"11","author":"K De","year":"2010","unstructured":"Chaudhuri, De K (2010) Fuzzy support vector machine for bankruptcy prediction. Appl Soft Comput 11 (1):2472\u20132486","journal-title":"Appl Soft Comput"},{"issue":"2","key":"1204_CR2","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(2):273\u2013297","journal-title":"Mach Learn"},{"issue":"1","key":"1204_CR3","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1109\/72.991432","volume":"13","author":"C-F Lin","year":"2002","unstructured":"Lin C-F, Wang S-D (2002) Fuzzy support vector machines. IEEE Trans Neural Netw 13(1):464\u2013471","journal-title":"IEEE Trans Neural Netw"},{"key":"1204_CR4","doi-asserted-by":"crossref","unstructured":"Burges CJC (1998) Geometry and invariance in kernel based methods. In: Scholkopf B, Burges CJC, Smola AJ (eds) Advances in kernel methods-support vector learning. MIT, Cambridge","DOI":"10.7551\/mitpress\/1130.003.0010"},{"key":"1204_CR5","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"key":"1204_CR6","first-page":"11","volume":"Article ID 2826","author":"D Tomar","year":"2014","unstructured":"Tomar D, Ojha D, Agarwal S (2014) An emotion detection system based on multi least squares twin support vector machine. Adv Artif Intell Article ID 282659:11","journal-title":"Adv Artif Intell"},{"key":"1204_CR7","doi-asserted-by":"crossref","unstructured":"Tomar D, Agarwal S (2015) Hybrid feature selection based weighted least squares twin support vector machine approach for diagnosing breast cancer, hepatitis, and diabetes. Adv Artif Neural Syst. (Article ID 265637), 10","DOI":"10.1155\/2015\/265637"},{"key":"1204_CR8","doi-asserted-by":"crossref","unstructured":"Tsujinishi D, Abe S (2003) Fuzzy least squares support vector machines. In: Proceedings of the international joint conference on neural networks. Portland, pp 1599\u20131604","DOI":"10.1109\/IJCNN.2003.1223938"},{"key":"1204_CR9","unstructured":"Tian D-Z, Peng G-B, Ha M-H (2012) Fuzzy support vector machine based on non-equilibrium data. In: International conference on machine learning and cybernetics. Xi\u2019an, pp 15\u201317"},{"key":"1204_CR10","unstructured":"Borovikov E (2005) An evaluation of support vector machines as a pattern recognition tool. University of Maryland at College Park. http:\/\/www.umiacs.umd.edu\/users\/yab\/SVMForPatternRecognition\/report.pdf"},{"key":"1204_CR11","doi-asserted-by":"crossref","unstructured":"Osuna E, Freund R, Girosi F (1997) Training support vector machines: an application to face detection. In: Proceedings of 1997 IEEE computer society conference on computer vision and pattern recognition. IEEE, pp 130\u2013136","DOI":"10.1109\/CVPR.1997.609310"},{"key":"1204_CR12","unstructured":"Golub GH, Van Loan C (1996) F, Matrix computations, 3rd edn. The John Hopkins University Press"},{"issue":"2\u20133","key":"1204_CR13","first-page":"255","volume":"17","author":"J Alcal\u00e1-Fdez","year":"2011","unstructured":"Alcal\u00e1-Fdez J, Fernandez A, Luengo J, Derrac J, Garc\u00eda S, S\u00e1nchez L, Herrera F (2011) KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Multiple-Valued Logic Soft Comput 17(2\u20133):255\u2013287","journal-title":"J Multiple-Valued Logic Soft Comput"},{"key":"1204_CR14","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1109\/TPAMI.2007.1068","volume":"29","author":"RK Jayadeva","year":"2007","unstructured":"Jayadeva RK, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell (TPAMI) 29:905\u2013910","journal-title":"IEEE Trans Pattern Anal Mach Intell (TPAMI)"},{"key":"1204_CR15","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1023\/A:1018628609742","volume":"9","author":"JAK Suykens","year":"1999","unstructured":"Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9:293\u2013300","journal-title":"Neural Process Lett"},{"issue":"1","key":"1204_CR16","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/S0925-2312(01)00644-0","volume":"48","author":"JAK Suykens","year":"2002","unstructured":"Suykens JAK, De Brabanter J, Lukas L, Vandewalle J (2002) Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing 48(1):85\u2013105","journal-title":"Neurocomputing"},{"key":"1204_CR17","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1109\/TPAMI.1985.4767725","volume":"6","author":"J Keller","year":"1985","unstructured":"Keller J, Hunt D (1985) Incorporating fuzzy membership functions into the perceptron algorithm. IEEE Trans Pattern Anal Mach Intell 6:693\u2013699","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1204_CR18","unstructured":"Sartakhti JS, Ghadiri N, Afrabandpey H, Yousefnezhad N (2016) Fuzzy Least squares twin support vector machines. arXiv: 1505.05451"},{"key":"1204_CR19","doi-asserted-by":"crossref","unstructured":"Zhang J, Liu Y (2004) Cervical cancer detection using SVM-based feature screening. In: Proceedings of the seventh international conference on medical image computing and computer aided intervention, pp 873\u2013880","DOI":"10.1007\/978-3-540-30136-3_106"},{"issue":"3","key":"1204_CR20","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/s00778-006-0002-5","volume":"16","author":"L Khan","year":"2007","unstructured":"Khan L, Awad M, Thuraisingham B (2007) A new intrusion detection system using support vector machines and hierarchical clustering. Int J Very Large Data Bases 16(3):507\u2013521","journal-title":"Int J Very Large Data Bases"},{"issue":"3","key":"1204_CR21","doi-asserted-by":"publisher","first-page":"7535","DOI":"10.1016\/j.eswa.2008.09.066","volume":"36","author":"MA Kumar","year":"2009","unstructured":"Kumar MA, Gopal M (2009) Least squares twin support vector machines for pattern classification. Expert Syst Appl 36(3):7535\u20137543","journal-title":"Expert Syst Appl"},{"key":"1204_CR22","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.neucom.2015.02.013","volume":"159","author":"S Mehrkanoon","year":"2015","unstructured":"Mehrkanoon S, Suykens JAK (2015) Learning solutions to partial differential equations using LS-SVM. Neurocomputing 159:105\u2013116","journal-title":"Neurocomputing"},{"key":"1204_CR23","doi-asserted-by":"crossref","unstructured":"Schmidt M, Gish H (1996) Speaker identification via support vector classifiers. In: Conference proceedings of 1996 IEEE international conference on acoustics, speech, and signa processing, 1996, ICASSP-96, vol 1. Atlanta, pp 105\u2013108","DOI":"10.1109\/ICASSP.1996.540301"},{"issue":"1","key":"1204_CR24","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/s10489-015-0751-1","volume":"45","author":"Mohammad Tanveer","year":"2016","unstructured":"Tanveer M, Khan MA, Ho S-S (2016) Robust energy-based least squares twin support vector machines. Appl Intell, https:\/\/doi.org\/10.1007\/s10489-015-0751-1","journal-title":"Applied Intelligence"},{"key":"1204_CR25","volume-title":"An introduction to support vector machines: and other kernel-based learning methods","author":"N Cristianini","year":"1999","unstructured":"Cristianini N, Taylor JS (1999) An introduction to support vector machines: and other kernel-based learning methods. Cambridge University Press, New York"},{"key":"1204_CR26","doi-asserted-by":"crossref","unstructured":"Mangasarian OL (1994) Nonlinear programming. SIAM","DOI":"10.1137\/1.9781611971255"},{"key":"1204_CR27","doi-asserted-by":"crossref","unstructured":"Phillips PJ (1998) Support vector machines applied to face recognition. In: Proceedings conference advances in neural information processing systems, vol 11, pp 803\u2013809","DOI":"10.6028\/NIST.IR.6241"},{"key":"1204_CR28","unstructured":"Murphy PM, Aha DW (1992) UCI repository of machine learning databases. University of California, Irvine. http:\/\/www.ics.uci.edu\/~mlearn"},{"key":"1204_CR29","doi-asserted-by":"crossref","unstructured":"Michel P, el Kaliouby R (2003) Real time facial expression recognition in video using support vector machines. In: Proceedings of the 5th international conference on multimodal interfaces, pp 258\u2013264, ISBN: 1-58113-621-8","DOI":"10.1145\/958432.958479"},{"key":"1204_CR30","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.knosys.2016.09.032","volume":"115","author":"Q Fan","year":"2017","unstructured":"Fan Q, Wang Z, Li D, Gao D, Zha H (2017) Entropy-based fuzzy support vector machine for imbalanced datasets. Knowl-Based Syst 115:87\u201399","journal-title":"Knowl-Based Syst"},{"key":"1204_CR31","first-page":"673","volume":"2","author":"Q Tong","year":"2005","unstructured":"Tong Q, Zheng H, Wang X (2005) Gene prediction algorithm based on the statistical combination and the classification in terms of gene characteristics. Int Conf Neural Netw Brain 2:673\u2013677","journal-title":"Int Conf Neural Netw Brain"},{"issue":"2","key":"1204_CR32","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1109\/TFUZZ.2010.2042721","volume":"18","author":"R Batuwita","year":"2010","unstructured":"Batuwita R, Palade V (2010) FSVM-CIL: fuzzy support vector machines for class imbalance learning. IEEE Trans Fuzzy Syst 18(2):558\u2013571","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1204_CR33","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/S0305-0483(03)00016-1","volume":"31","author":"R Malhotra","year":"2003","unstructured":"Malhotra R, Malhotra DK (2003) Evaluating consumer loans using neural networks. Omega 31:83\u201396","journal-title":"Omega"},{"issue":"1","key":"1204_CR34","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1007\/s10489-016-0886-8","volume":"47","author":"Reshma Rastogi","year":"2017","unstructured":"Rastogi R, Saigal P (2017) Tree-based localized fuzzy twin support vector clustering with square loss function. Applied Intelligence. https:\/\/doi.org\/10.1007\/s10489-016-0886-8","journal-title":"Applied Intelligence"},{"issue":"1","key":"1204_CR35","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1007\/s10489-016-0809-8","volume":"46","author":"S Balasundaram","year":"2017","unstructured":"Balasundaram S, Gupta D, Prasad SC (2017) A new approach for training Lagrangian twin support vector machine via unconstrained convex minimization. Appl Intell 46(1):124\u2013134","journal-title":"Appl Intell"},{"key":"1204_CR36","unstructured":"Gunn SR (1998) Support vector machines for classification and regression. ISIS technical report 14, University of Southampton"},{"key":"1204_CR37","doi-asserted-by":"publisher","first-page":"5723","DOI":"10.1109\/TIP.2015.2484068","volume":"24","author":"S Zhang","year":"2015","unstructured":"Zhang S, Zhao S, Sui Y, Zhang L (2015) Single object tracking with fuzzy least squares support vector machine. IEEE Trans Image Process 24:5723\u20135738","journal-title":"IEEE Trans Image Process"},{"issue":"2","key":"1204_CR38","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1007\/s10489-016-0858-z","volume":"46","author":"VN Phu","year":"2017","unstructured":"Phu VN, Dat ND, Tran VTN, Chau VTN, Nguyen TA (2017) Fuzzy C-means for english sentiment classification in a distributed system. Appl Intell 46(2):717\u2013738","journal-title":"Appl Intell"},{"key":"1204_CR39","volume-title":"Statistical learning theory","author":"VN Vapnik","year":"1998","unstructured":"Vapnik VN (1998) Statistical learning theory. Wiley, New York"},{"issue":"9","key":"1204_CR40","doi-asserted-by":"publisher","first-page":"1553","DOI":"10.1007\/s13042-017-0664-x","volume":"9","author":"Su-Gen Chen","year":"2017","unstructured":"Chen S, Wu X (2017) A new fuzzy support vector machine for pattern classification. Int J Mach Learn Cybern. https:\/\/doi.org\/10.1007\/s13042-017-0664-x","journal-title":"International Journal of Machine Learning and Cybernetics"},{"issue":"9","key":"1204_CR41","doi-asserted-by":"publisher","first-page":"3158","DOI":"10.1016\/j.patcog.2014.03.008","volume":"47","author":"Y Shao","year":"2014","unstructured":"Shao Y, Chen W, Zhang J, Wang Z, Deng N (2014) An efficient weighted Lagrangian twin support vector machine for imbalanced data classification. Pattern Recogn 47(9):3158\u20133167","journal-title":"Pattern Recogn"},{"issue":"6","key":"1204_CR42","doi-asserted-by":"publisher","first-page":"2299","DOI":"10.1016\/j.patcog.2011.11.028","volume":"45","author":"YH Shao","year":"2012","unstructured":"Shao YH, Deng NY, Yang ZM (2012) Least squares recursive projection twin support vector machine for classification. Pattern Recogn 45(6):2299\u20132307","journal-title":"Pattern Recogn"},{"key":"1204_CR43","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.knosys.2014.10.011","volume":"73","author":"YH Shao","year":"2015","unstructured":"Shao YH, Chen WJ, Wang Z, Li CN, Deng NY (2015) Weighted linear loss twin support vector machine for large-scale classification. Knowl-Based Syst 73:276\u2013288","journal-title":"Knowl-Based Syst"},{"key":"1204_CR44","unstructured":"Bao Y-K, Liu Z-T, Guo L, Wang W (2005) Forecasting stock composite index by fuzzy support vector machines regression. In: Proceeding of international conference on machine learning and cybernetics, vol 6, pp 3535\u20133540"},{"issue":"6","key":"1204_CR45","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1109\/TFUZZ.2005.859320","volume":"13","author":"Y Wang","year":"2005","unstructured":"Wang Y, Wang S, Lai KK (2005) A new fuzzy support vector machine to evaluate credit risk. IEEE Trans Fuzzy Syst 13(6):820\u2013831","journal-title":"IEEE Trans Fuzzy Syst"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10489-018-1204-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-018-1204-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-018-1204-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,2]],"date-time":"2023-09-02T23:23:48Z","timestamp":1693697028000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10489-018-1204-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,2]]},"references-count":45,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2018,11]]}},"alternative-id":["1204"],"URL":"https:\/\/doi.org\/10.1007\/s10489-018-1204-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,2]]},"assertion":[{"value":"2 June 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}