{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T14:29:31Z","timestamp":1784730571045,"version":"3.55.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T00:00:00Z","timestamp":1715904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T00:00:00Z","timestamp":1715904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Fuzzy Syst."],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s40815-024-01725-z","type":"journal-article","created":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T10:09:36Z","timestamp":1715940576000},"page":"2750-2766","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Fuzzy Twin Support Vector Machine Based on Dissimilarity Measure and Its Biomedical Applications"],"prefix":"10.1007","volume":"26","author":[{"given":"Jianxiang","family":"Qiu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialiang","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongxiao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruping","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingwei","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,17]]},"reference":[{"issue":"10","key":"1725_CR1","doi-asserted-by":"publisher","first-page":"3889","DOI":"10.1007\/s12652-018-1160-1","volume":"10","author":"A Anagaw","year":"2019","unstructured":"Anagaw, A., Chang, Y.L.: A new complement Na\u00efve Bayesian approach for biomedical data classification. J. Ambient. Intell. Humaniz. Comput. 10(10), 3889\u20133897 (2019)","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"1725_CR2","doi-asserted-by":"crossref","unstructured":"Aryal, S., Ting, K.M., Haffari, G., Washio, T.: MP-dissimilarity: a data dependent dissimilarity measure. In: 2014 IEEE International Conference on Data Mining, IEEE. pp. 707\u2013712 (2014)","DOI":"10.1109\/ICDM.2014.33"},{"issue":"2","key":"1725_CR3","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1007\/s10115-017-1046-0","volume":"53","author":"S Aryal","year":"2017","unstructured":"Aryal, S., Ting, K.M., Washio, T., Haffari, G.: Data-dependent dissimilarity measure: an effective alternative to geometric distance measures. Knowl. Inf. Syst. 53(2), 479\u2013506 (2017)","journal-title":"Knowl. Inf. Syst."},{"key":"1725_CR4","unstructured":"Asuncion, A., Newman, D.: UCI machine learning repository. (2007)"},{"key":"1725_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108331","volume":"122","author":"J Bai","year":"2022","unstructured":"Bai, J., Li, Y., Li, J., Yang, X., Jiang, Y., Xia, S.T.: Multinomial random forest. Pattern Recogn. 122, 108331 (2022)","journal-title":"Pattern Recogn."},{"issue":"1","key":"1725_CR6","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"1725_CR7","unstructured":"Chen, T.Q., He, T.: Xgboost: extreme gradient boosting. R package version 04-2. 1(4), 1\u20134 (2015)"},{"issue":"6","key":"1725_CR8","first-page":"2540","volume":"34","author":"H Das","year":"2022","unstructured":"Das, H., Naik, B., Behera, H.S., Jaiswal, S., Mahato, P., Rout, M.: Biomedical data analysis using neuro-fuzzy model with post-feature reduction. J. King Saud Univ.-Comput. Inf. Sci. 34(6), 2540\u20132550 (2022)","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"issue":"11","key":"1725_CR9","first-page":"3321","volume":"31","author":"S Ding","year":"2020","unstructured":"Ding, S., Xu, X., Wang, Y.: Optimized density peaks clustering algorithm based on dissimilarity measure. J. Softw. 31(11), 3321\u20133333 (2020)","journal-title":"J. Softw."},{"key":"1725_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107933","volume":"113","author":"MA Ganaie","year":"2021","unstructured":"Ganaie, M.A., Tanveer, M.: Alzheimer\u2019s disease neuroimaging initiative: fuzzy least squares projection twin support vector machines for class imbalance learning. Appl. Soft Comput. 113, 107933 (2021)","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"1725_CR11","first-page":"1","volume":"36","author":"MA Ganaie","year":"2022","unstructured":"Ganaie, M.A., Kumari, A., Malik, A.K., Tanveer, M.: EEG signal classification using improved intuitionistic fuzzy twin support vector machines. Neural Comput. Appl. 36(1), 1\u201317 (2022)","journal-title":"Neural Comput. Appl."},{"issue":"11","key":"1725_CR12","doi-asserted-by":"publisher","first-page":"4815","DOI":"10.1109\/TFUZZ.2022.3161729","volume":"30","author":"M Ganaie","year":"2022","unstructured":"Ganaie, M., Tanveer, M., Lin, C.T.: Large-scale fuzzy least squares twin SVMS for class imbalance learning. IEEE Trans. Fuzzy Syst. 30(11), 4815\u20134827 (2022)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"1725_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110899","volume":"149","author":"MA Ganaie","year":"2023","unstructured":"Ganaie, M.A., Kumari, A., Girard, A., Kasa-Vubu, J., Tanveer, M.: Alzheimer\u2019s disease neuroimaging initiative: diagnosis of Alzheimer\u2019s disease via intuitionistic fuzzy least squares twin SVM. Appl. Soft Comput. 149, 110899 (2023)","journal-title":"Appl. Soft Comput."},{"key":"1725_CR14","doi-asserted-by":"crossref","unstructured":"Gao, B.B., Wang, J.J., Wang, Y., Yang, C.Y.: Coordinate descent fuzzy twin support vector machine for classification. In: 2015 IEEE 14th international conference on machine learning and applications (ICMLA), IEEE. pp. 7\u201312 (2015)","DOI":"10.1109\/ICMLA.2015.35"},{"key":"1725_CR15","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.neunet.2019.12.001","volume":"123","author":"C Gautam","year":"2020","unstructured":"Gautam, C., Mishra, P.K., Tiwari, A., Richhariya, B., Pandey, H.M., Wang, S.H., Tanveer, M.: Alzheimer\u2019s disease neuroimaging initiative: minimum variance-embedded deep kernel regularized least squares method for one-class classification and its applications to biomedical data. Neural Netw. 123, 191\u2013216 (2020)","journal-title":"Neural Netw."},{"issue":"11","key":"1725_CR16","doi-asserted-by":"publisher","first-page":"7153","DOI":"10.1007\/s00521-018-3551-9","volume":"31","author":"D Gupta","year":"2019","unstructured":"Gupta, D., Richhariya, B., Borah, P.: A fuzzy twin support vector machine based on information entropy for class imbalance learning. Neural Comput. Appl. 31(11), 7153\u20137164 (2019)","journal-title":"Neural Comput. Appl."},{"issue":"14","key":"1725_CR17","doi-asserted-by":"publisher","first-page":"11335","DOI":"10.1007\/s00521-021-05866-2","volume":"34","author":"D Gupta","year":"2022","unstructured":"Gupta, D., Borah, P., Sharma, U.M., Prasad, M.: Data-driven mechanism based on fuzzy Lagrangian twin parametric-margin support vector machine for biomedical data analysis. Neural Comput. Appl. 34(14), 11335\u201311345 (2022)","journal-title":"Neural Comput. Appl."},{"key":"1725_CR18","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.fss.2022.06.009","volume":"449","author":"U Gupta","year":"2022","unstructured":"Gupta, U., Gupta, D.: Bipolar fuzzy based least squares twin bounded support vector machine. Fuzzy Sets Syst. 449, 120\u2013161 (2022)","journal-title":"Fuzzy Sets Syst."},{"issue":"9","key":"1725_CR19","doi-asserted-by":"publisher","first-page":"4243","DOI":"10.1007\/s00521-020-05240-8","volume":"33","author":"BB Hazarika","year":"2021","unstructured":"Hazarika, B.B., Gupta, D.: Density-weighted support vector machines for binary class imbalance learning. Neural Comput. Appl. 33(9), 4243\u20134261 (2021)","journal-title":"Neural Comput. Appl."},{"key":"1725_CR20","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1002\/9781118548387","volume-title":"Applied Logistic Regression","author":"DW Hosmer","year":"2013","unstructured":"Hosmer, D.W., Lemeshow, S., Sturdivant, R.X.: Applied Logistic Regression, p. 398. Wiley, Hoboken (2013)"},{"issue":"2","key":"1725_CR21","first-page":"93","volume":"18","author":"H Ju","year":"2021","unstructured":"Ju, H., Qiang, W., Jing, L.: A novel interval-valued fuzzy multiple twin support vector machine. Iran. J. Fuzzy Syst. 18(2), 93\u2013107 (2021)","journal-title":"Iran. J. Fuzzy Syst."},{"key":"1725_CR22","first-page":"1","volume":"30","author":"GL Ke","year":"2017","unstructured":"Ke, G.L., Finley, T., Wang, T.F., Chen, W., Ma, W.D., Ye, Q.W., Liu, T.Y.: Lightgbm: a highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30, 1\u20139 (2017)","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"5","key":"1725_CR23","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1109\/TPAMI.2007.1068","volume":"29","author":"R Khemchandani","year":"2007","unstructured":"Khemchandani, R., Chandra, S.: Twin support vector machines for pattern classification. IEEE Trans. Pattern Anal. Mach. Intell. 29(5), 905\u2013910 (2007)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"1725_CR24","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1037\/0033-295X.85.5.445","volume":"85","author":"CL Krumhansl","year":"1978","unstructured":"Krumhansl, C.L.: Concerning the applicability of geometric models to similarity data: the interrelationship between similarity and spatial density. Psychol. Rev. 85(5), 445\u2013463 (1978)","journal-title":"Psychol. Rev."},{"key":"1725_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.108231","volume":"115","author":"ZZ Liang","year":"2022","unstructured":"Liang, Z.Z., Lei, Z.: Intuitionistic fuzzy twin support vector machines with the insensitive pinball loss. Appl. Soft Comput. 115, 108231 (2022)","journal-title":"Appl. Soft Comput."},{"issue":"2","key":"1725_CR26","doi-asserted-by":"publisher","first-page":"bbad054","DOI":"10.1093\/bib\/bbad054","volume":"24","author":"MZ Liu","year":"2023","unstructured":"Liu, M.Z., Zhou, J., Xi, Q., Liang, Y.C., Li, H.C., Liang, P.F., Guo, Y.T., Liu, M., Temuqile, T., Yang, L., Zou, Y.C.: A computational framework of routine test data for the cost-effective chronic disease prediction. Brief. Bioinf. 24(2), bbad054 (2023)","journal-title":"Brief. Bioinf."},{"key":"1725_CR27","first-page":"1","volume":"31","author":"L Prokhorenkova","year":"2018","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., Gulin, A.: CatBoost: unbiased boosting with categorical features. Adv. Neural Inf. Process. Syst. 31, 1\u201311 (2018)","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"1725_CR28","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1108\/IJICC-08-2023-0208","volume":"17","author":"JX Qiu","year":"2023","unstructured":"Qiu, J.X., Xie, J.L., Zhang, D.X., Zhang, R.P.: A robust twin support vector machine based on fuzzy systems. Int. J. Intell. Comput. Cybern. 17(1), 101\u201325 (2023)","journal-title":"Int. J. Intell. Comput. Cybern."},{"key":"1725_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109287","volume":"137","author":"Z Rasool","year":"2023","unstructured":"Rasool, Z., Aryal, S., Bouadjenek, M.R., Dazeley, R.: Overcoming weaknesses of density peak clustering using a data-dependent similarity measure. Pattern Recogn. 137, 109287 (2023)","journal-title":"Pattern Recogn."},{"key":"1725_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108992","volume":"133","author":"J Ren","year":"2023","unstructured":"Ren, J., Wang, Y., Cheung, Y.M., Gao, X.Z., Guo, X.: Grouping-based oversampling in kernel space for imbalanced data classification. Pattern Recogn. 133, 108992 (2023)","journal-title":"Pattern Recogn."},{"issue":"11","key":"1725_CR31","doi-asserted-by":"publisher","first-page":"2140","DOI":"10.1109\/TFUZZ.2019.2893863","volume":"27","author":"S Rezvani","year":"2019","unstructured":"Rezvani, S., Wang, X., Pourpanah, F.: Intuitionistic fuzzy twin support vector machines. IEEE Trans. Fuzzy Syst. 27(11), 2140\u20132151 (2019)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"1725_CR32","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.eswa.2018.03.053","volume":"106","author":"B Richhariya","year":"2018","unstructured":"Richhariya, B., Tanveer, M.: EEG signal classification using universum support vector machine. Expert Syst. Appl. 106, 169\u2013182 (2018)","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"1725_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3387131","volume":"21","author":"B Richhariya","year":"2021","unstructured":"Richhariya, B., Tanveer, M.: Alzheimer\u2019s disease neuroimaging initiative: an efficient angle-based universum least squares twin support vector machine for classification. ACM Trans. Internet Technol. (TOIT) 21(3), 1\u201324 (2021)","journal-title":"ACM Trans. Internet Technol. (TOIT)"},{"issue":"14","key":"1725_CR34","doi-asserted-by":"publisher","first-page":"11411","DOI":"10.1007\/s00521-021-05721-4","volume":"34","author":"B Richhariya","year":"2022","unstructured":"Richhariya, B., Tanveer, M.: Alzheimer\u2019s disease neuroimaging initiative: a fuzzy universum least squares twin support vector machine (FULSTSVM). Neural Comput. Appl. 34(14), 11411\u201311422 (2022)","journal-title":"Neural Comput. Appl."},{"issue":"7","key":"1725_CR35","doi-asserted-by":"publisher","first-page":"4400","DOI":"10.1109\/TCYB.2022.3165879","volume":"53","author":"M Tanveer","year":"2023","unstructured":"Tanveer, M., Ganaie, M.A., Bhattacharjee, A., Lin, C.T.: Intuitionistic fuzzy weighted least squares twin SVMs. IEEE Trans. Cybern. 53(7), 4400\u20134409 (2023)","journal-title":"IEEE Trans. Cybern."},{"key":"1725_CR36","doi-asserted-by":"crossref","unstructured":"Ting, K.M., Zhu, Y., Carman, M., Zhu, Y., Zhou, Z.H.: Overcoming key weaknesses of distance-based neighbourhood methods using a data dependent dissimilarity measure. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. pp. 1205\u20131214 (2016)","DOI":"10.1145\/2939672.2939779"},{"key":"1725_CR37","doi-asserted-by":"crossref","unstructured":"Wang, H., Gupta, G.: Fold-r++: a scalable toolset for automated inductive learning of default theories from mixed data. In: International Symposium on Functional and Logic Programming, Springer. pp. 224\u2013242 (2022)","DOI":"10.1007\/978-3-030-99461-7_13"},{"issue":"5","key":"1725_CR38","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1017\/S1471068422000205","volume":"22","author":"H Wang","year":"2022","unstructured":"Wang, H., Shakerin, F., Gupta, G.: Fold-rm: a scalable, efficient, and explainable inductive learning algorithm for multi-category classification of mixed data. Theory Pract. Logic Program. 22(5), 658\u2013677 (2022)","journal-title":"Theory Pract. Logic Program."},{"issue":"2","key":"1725_CR39","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1109\/TNNLS.2015.2513006","volume":"28","author":"Y Xu","year":"2016","unstructured":"Xu, Y., Yang, Z., Pan, X.: A novel twin support-vector machine with pinball loss. IEEE Trans. Neural Netw. Learn. Syst. 28(2), 359\u2013370 (2016)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"1","key":"1725_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12938-018-0604-3","volume":"17","author":"L Zhang","year":"2018","unstructured":"Zhang, L., Yang, H., Jiang, Z.: Imbalanced biomedical data classification using self-adaptive multilayer elm combined with dynamic GAN. Biomed. Eng. Online 17(1), 1\u201321 (2018)","journal-title":"Biomed. Eng. Online"},{"issue":"2","key":"1725_CR41","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1007\/s12539-021-00489-6","volume":"14","author":"Y Zou","year":"2021","unstructured":"Zou, Y., Ding, Y., Peng, L., Zou, Q.: FTWSVM-SR: DNA-binding proteins identification via fuzzy twin support vector machines on self-representation. Interdiscip. Sci. 14(2), 372\u2013384 (2021)","journal-title":"Interdiscip. Sci."}],"container-title":["International Journal of Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40815-024-01725-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40815-024-01725-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40815-024-01725-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T14:08:12Z","timestamp":1737641292000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40815-024-01725-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,17]]},"references-count":41,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["1725"],"URL":"https:\/\/doi.org\/10.1007\/s40815-024-01725-z","relation":{},"ISSN":["1562-2479","2199-3211"],"issn-type":[{"value":"1562-2479","type":"print"},{"value":"2199-3211","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,17]]},"assertion":[{"value":"14 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}]}}