{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:46:19Z","timestamp":1765547179003,"version":"3.37.3"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:00:00Z","timestamp":1700524800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:00:00Z","timestamp":1700524800000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s00521-023-09144-1","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T05:30:57Z","timestamp":1700544657000},"page":"2479-2491","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Subconcept perturbation-based classifier for within-class multimodal data"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7714-2283","authenticated-orcid":false,"given":"George D. C.","family":"Cavalcanti","sequence":"first","affiliation":[]},{"given":"Rodolfo J. O.","family":"Soares","sequence":"additional","affiliation":[]},{"given":"Edson L.","family":"Ara\u00fajo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"9144_CR1","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1016\/j.neucom.2016.07.017","volume":"214","author":"ER Silva","year":"2016","unstructured":"Silva ER, Cavalcanti GDC, Ren TI (2016) Class-wise feature extraction technique for multimodal data. Neurocomputing 214:1001\u20131010","journal-title":"Neurocomputing"},{"key":"9144_CR2","doi-asserted-by":"crossref","unstructured":"Sugiyama M, Cohen WW, Moore AW (2006) Local fisher discriminant analysis for supervised dimensionality reduction. In: Cohen WW, Moore AW ((eds) ICML, ACM international conference proceeding series, vol 148, pp 905\u2013912. http:\/\/dblp.uni-trier.de\/db\/journals\/ijon\/ijon214.html","DOI":"10.1145\/1143844.1143958"},{"key":"9144_CR3","first-page":"1027","volume":"8","author":"M Sugiyama","year":"2007","unstructured":"Sugiyama M (2007) Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J Mach Learn Res 8:1027\u20131061","journal-title":"J Mach Learn Res"},{"key":"9144_CR4","doi-asserted-by":"publisher","first-page":"3077","DOI":"10.1016\/j.neucom.2009.03.014","volume":"72","author":"T Wang","year":"2009","unstructured":"Wang T, Tian S, Huang H, Deng D (2009) Learning by local kernel polarization. Neurocomputing 72:3077\u20133084","journal-title":"Neurocomputing"},{"key":"9144_CR5","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1111\/coin.12128","volume":"34","author":"S Sharma","year":"2018","unstructured":"Sharma S, Somayaji A, Japkowicz N (2018) Learning over subconcepts: strategies for 1-class classification. Comput Intell 34:440\u2013467","journal-title":"Comput Intell"},{"key":"9144_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.106994","author":"M Taheri","year":"2019","unstructured":"Taheri M, Moslehi Z, Mirzaei A, Safayani M (2019) A self-adaptive local metric learning method for classification. Pattern Recognit. https:\/\/doi.org\/10.1016\/j.patcog.2019.106994","journal-title":"Pattern Recognit"},{"key":"9144_CR7","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.ins.2013.12.019","volume":"264","author":"B Krawczyk","year":"2014","unstructured":"Krawczyk B, Wozniak M, Cyganek B (2014) Clustering-based ensembles for one-class classification. Inf Sci 264:182\u2013195","journal-title":"Inf Sci"},{"key":"9144_CR8","doi-asserted-by":"publisher","first-page":"238","DOI":"10.3390\/info9090238","volume":"9","author":"H Guo","year":"2018","unstructured":"Guo H, Zhou J, Wu CA (2018) Imbalanced learning based on data-partition and smote. Information 9:238","journal-title":"Information"},{"key":"9144_CR9","doi-asserted-by":"publisher","first-page":"134","DOI":"10.3390\/a14050134","volume":"14","author":"L Abdallah","year":"2021","unstructured":"Abdallah L, Badarna M, Khalifa W, Yousef M (2021) Multikoc: multi-one-class classifier based k-means clustering. Algorithms 14:134","journal-title":"Algorithms"},{"key":"9144_CR10","doi-asserted-by":"crossref","unstructured":"Liu Y, Li Z, Xiong H, Gao X, Wu J (2010) Understanding of internal clustering validation measures. In: Society IC (eds) Proceedings of the 2010 IEEE international conference on data mining, vol 10. ICDM, Washington, DC, USA, pp 911\u2013916","DOI":"10.1109\/ICDM.2010.35"},{"key":"9144_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107290","volume":"228","author":"RC Fragoso","year":"2021","unstructured":"Fragoso RC, Cavalcanti GD, Pinheiro RH, Oliveira LS (2021) Dynamic selection and combination of one-class classifiers for multi-class classification. Knowl Based Syst 228:107290","journal-title":"Knowl Based Syst"},{"key":"9144_CR12","doi-asserted-by":"publisher","first-page":"21877","DOI":"10.1007\/s00521-022-07647-x","volume":"34","author":"CG Marcelino","year":"2022","unstructured":"Marcelino CG, Pedreira CE (2022) Feature space partition: a local-global approach for classification. Neural Comput Appl 34:21877\u201321890. https:\/\/doi.org\/10.1007\/s00521-022-07647-x","journal-title":"Neural Comput Appl"},{"key":"9144_CR13","doi-asserted-by":"publisher","first-page":"6247","DOI":"10.1007\/s00521-020-05395-4","volume":"33","author":"AE Ezugwu","year":"2021","unstructured":"Ezugwu AE et al (2021) Automatic clustering algorithms: a systematic review and bibliometric analysis of relevant literature. Neural Comput Appl 33:6247\u20136306. https:\/\/doi.org\/10.1007\/s00521-020-05395-4","journal-title":"Neural Comput Appl"},{"key":"9144_CR14","doi-asserted-by":"publisher","first-page":"10987","DOI":"10.1007\/s00521-020-05649-1","volume":"33","author":"BA Hassan","year":"2021","unstructured":"Hassan BA, Rashid TA (2021) A multidisciplinary ensemble algorithm for clustering heterogeneous datasets. Neural Comput Appl 33:10987\u201311010. https:\/\/doi.org\/10.1007\/s00521-020-05649-1","journal-title":"Neural Comput Appl"},{"key":"9144_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-023-08415-1","author":"H Zhang","year":"2023","unstructured":"Zhang H, Li P, Meng F, Fan W, Xue Z (2023) Mapreduce-based distributed tensor clustering algorithm. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-023-08415-1","journal-title":"Neural Comput Appl"},{"key":"9144_CR16","doi-asserted-by":"publisher","first-page":"21139","DOI":"10.1007\/s00521-022-07595-6","volume":"34","author":"SA Mousavian Anaraki","year":"2022","unstructured":"Mousavian Anaraki SA, Haeri A, Moslehi F (2022) Generating balanced and strong clusters based on balance-constrained clustering approach (strong balance-constrained clustering) for improving ensemble classifier performance. Neural Comput Appl 34:21139\u201321155. https:\/\/doi.org\/10.1007\/s00521-022-07595-6","journal-title":"Neural Comput Appl"},{"key":"9144_CR17","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s00521-021-05873-3","volume":"34","author":"A Karna","year":"2022","unstructured":"Karna A, Gibert K (2022) Automatic identification of the number of clusters in hierarchical clustering. Neural Comput Appl 34:119\u2013134. https:\/\/doi.org\/10.1007\/s00521-021-05873-3","journal-title":"Neural Comput Appl"},{"key":"9144_CR18","doi-asserted-by":"publisher","first-page":"11459","DOI":"10.1007\/s00521-019-04636-5","volume":"32","author":"N Nidheesh","year":"2020","unstructured":"Nidheesh N, Nazeer KAA, Ameer PM (2020) A hierarchical clustering algorithm based on silhouette index for cancer subtype discovery from genomic data. Neural Comput Appl 32:11459\u201311476. https:\/\/doi.org\/10.1007\/s00521-019-04636-5","journal-title":"Neural Comput Appl"},{"key":"9144_CR19","doi-asserted-by":"publisher","first-page":"16565","DOI":"10.1007\/s00500-020-04960-2","volume":"24","author":"EL Ara\u00fajo","year":"2020","unstructured":"Ara\u00fajo EL, Cavalcanti GDC, Ren TI (2020) Perturbation-based classifier. Soft Comput 24:16565\u201316576","journal-title":"Soft Comput"},{"key":"9144_CR20","volume-title":"Pattern classification","author":"RO Duda","year":"2001","unstructured":"Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, Hoboken"},{"key":"9144_CR21","volume-title":"Introduction to statistical pattern recognition","author":"K Fukunaga","year":"1972","unstructured":"Fukunaga K (1972) Introduction to statistical pattern recognition. Academic Press, New York"},{"key":"9144_CR22","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/34.824819","volume":"22","author":"AK Jain","year":"2000","unstructured":"Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22:4\u201337. https:\/\/doi.org\/10.1109\/34.824819","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9144_CR23","volume-title":"Methods for incremental learning: a survey","author":"MRR Ade","year":"2013","unstructured":"Ade MRR, Deshmukh PR (2013) Methods for incremental learning: a survey. Semantic Scholar, New York"},{"key":"9144_CR24","doi-asserted-by":"publisher","first-page":"2165","DOI":"10.1109\/TSP.2004.830991","volume":"52","author":"J Kivinen","year":"2004","unstructured":"Kivinen J, Smola AJ, Williamson RC (2004) Online learning with kernels. IEEE Trans Signal Process 52:2165\u20132176","journal-title":"IEEE Trans Signal Process"},{"key":"9144_CR25","unstructured":"Lutz A, Rodner E, Denzler J (2011) Efficient multi-class incremental learning using gaussian processes. In: Open German\u2013Russian workshop on pattern recognition and image understanding, pp 182-185"},{"key":"9144_CR26","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1134\/S1054661813030103","volume":"23","author":"A L\u00fctz","year":"2013","unstructured":"L\u00fctz A, Rodner E, Denzler J (2013) I want to know more\u2013efficient multi-class incremental learning using gaussian processes. Pattern Recogn Image Anal 23:402\u2013407. https:\/\/doi.org\/10.1134\/S1054661813030103","journal-title":"Pattern Recogn Image Anal"},{"key":"9144_CR27","doi-asserted-by":"publisher","first-page":"105","DOI":"10.3390\/a10030105","volume":"10","author":"J H\u00e4m\u00e4l\u00e4inen","year":"2017","unstructured":"H\u00e4m\u00e4l\u00e4inen J, Jauhiainen S, K\u00e4rkk\u00e4inen T (2017) Comparison of internal clustering validation indices for prototype-based clustering. Algorithms 10:105","journal-title":"Algorithms"},{"key":"9144_CR28","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.patcog.2012.07.021","volume":"46","author":"O Arbelaitz","year":"2013","unstructured":"Arbelaitz O, Gurrutxaga I, Muguerza J, P\u00e8rez JM, Perona I (2013) An extensive comparative study of cluster validity indices. Pattern Recogn 46:243\u2013256","journal-title":"Pattern Recogn"},{"key":"9144_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/03610917408548446","volume":"3","author":"T Cali\u0144ski","year":"1974","unstructured":"Cali\u0144ski T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat Simul Comput 3:1\u201327","journal-title":"Commun Stat Simul Comput"},{"key":"9144_CR30","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","volume":"1","author":"DL Davies","year":"1979","unstructured":"Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1:224\u2013227","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"9144_CR31","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1111\/1467-9868.00293","volume":"63","author":"R Tibshirani","year":"2001","unstructured":"Tibshirani R, Guenther W, Hastie T (2001) Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc Ser B 63(2):411\u2013423","journal-title":"J R Stat Soc Ser B"},{"key":"9144_CR32","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53\u201365","journal-title":"J Comput Appl Math"},{"key":"9144_CR33","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.eswa.2019.01.074","volume":"125","author":"R \u00dcnl\u00fc","year":"2019","unstructured":"\u00dcnl\u00fc R, Xanthopoulos P (2019) Estimating the number of clusters in a dataset via consensus clustering. Expert Syst Appl 125:33\u201339","journal-title":"Expert Syst Appl"},{"issue":"1","key":"9144_CR34","first-page":"27","volume":"5","author":"E Rendon","year":"2011","unstructured":"Rendon E, Abundez I, Arizmendi A, Quiroz E (2011) Internal versus external cluster validation indexes. Int J Comput Commun 5(1):27\u201334","journal-title":"Int J Comput Commun"},{"key":"9144_CR35","first-page":"193","volume-title":"Data mining annals of information systems","author":"GM Weiss","year":"2010","unstructured":"Weiss GM (2010) The impact of small disjuncts on classifier learning. In: Stahlbock R, Crone SF, Lessmann S (eds) Data mining annals of information systems, vol 8. Springer, Cham, pp 193\u2013226"},{"key":"9144_CR36","first-page":"558","volume-title":"ICML","author":"GM Weiss","year":"1995","unstructured":"Weiss GM, Prieditis A (1995) Learning with rare cases and small disjuncts. In: Prieditis A, Russell SJ (eds) ICML. Morgan Kaufmann, Burlington, pp 558\u2013565"},{"key":"9144_CR37","doi-asserted-by":"publisher","first-page":"1641","DOI":"10.1016\/S0167-8655(03)00003-5","volume":"24","author":"Z He","year":"2003","unstructured":"He Z, Xu X (2003) Discovering cluster-based local outliers. Pattern Recogn Lett 24:1641\u20131650","journal-title":"Pattern Recogn Lett"},{"key":"9144_CR38","doi-asserted-by":"publisher","first-page":"1252","DOI":"10.1109\/TSMCB.2005.850183","volume":"35","author":"G Valentini","year":"2005","unstructured":"Valentini G (2005) An experimental bias-variance analysis of svm ensembles based on resampling techniques. IEEE Trans Syst Man Cybern Part B 35:1252\u20131271","journal-title":"IEEE Trans Syst Man Cybern Part B"},{"key":"9144_CR39","first-page":"255","volume":"17","author":"J Alcal\u00e1-Fdez","year":"2011","unstructured":"Alcal\u00e1-Fdez J, Fern\u00e1ndez A, Luengo J, Derrac J, Garc\u00eda S (2011) Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Multiple Valued Log. Soft Comput. 17:255\u2013287","journal-title":"J. Multiple Valued Log. Soft Comput."},{"key":"9144_CR40","unstructured":"Dua D, Graff C (2017) Uci machine learning repository. http:\/\/archive.ics.uci.edu\/ml"},{"key":"9144_CR41","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","volume":"30","author":"AP Bradley","year":"1997","unstructured":"Bradley AP (1997) The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recogn 30:1145\u20131159","journal-title":"Pattern Recogn"},{"key":"9144_CR42","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.ins.2013.07.007","volume":"250","author":"V L\u00f3pez","year":"2013","unstructured":"L\u00f3pez V, Fern\u00e1ndez A, Garc\u00eda S, Palade V, Herrera F (2013) An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf Sci 250:113\u2013141","journal-title":"Inf Sci"},{"key":"9144_CR43","first-page":"2","volume":"9","author":"B Cheng","year":"1994","unstructured":"Cheng B, Titterington DM (1994) Neural networks: a review from a statistical perspective. Stat Sci 9:2\u201330","journal-title":"Stat Sci"},{"key":"9144_CR44","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/a:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332. https:\/\/doi.org\/10.1023\/a:1010933404324","journal-title":"Mach Learn"},{"key":"9144_CR45","volume-title":"Adaptive computation and machine learning","author":"B Sch\u00f6lkopf","year":"2002","unstructured":"Sch\u00f6lkopf B, Smola AJ (2002) Learning with kernels:support vector machines, regularization, optimization, and beyond. Adaptive computation and machine learning. MIT Press, Cambridge"},{"key":"9144_CR46","first-page":"3133","volume":"15","author":"M Fern\u00e1ndez-Delgado","year":"2014","unstructured":"Fern\u00e1ndez-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15:3133\u20133181","journal-title":"J Mach Learn Res"},{"key":"9144_CR47","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","volume":"32","author":"M Friedman","year":"1937","unstructured":"Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32:675\u2013701. https:\/\/doi.org\/10.1080\/01621459.1937.10503522","journal-title":"J Am Stat Assoc"},{"key":"9144_CR48","doi-asserted-by":"publisher","first-page":"80","DOI":"10.2307\/3001968","volume":"1","author":"F Wilcoxon","year":"1945","unstructured":"Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1:80\u201383","journal-title":"Biom Bull"},{"key":"9144_CR49","first-page":"813","volume-title":"IJCAI","author":"RC Holte","year":"1989","unstructured":"Holte RC, Acker L, Porter BW, Sridharan NS (1989) Concept learning and the problem of small disjuncts. In: Sridharan NS (ed) IJCAI. Morgan Kaufmann, Burlington, pp 813\u2013818"},{"key":"9144_CR50","first-page":"665","volume-title":"AAAI\/IAAI","author":"GM Weiss","year":"2000","unstructured":"Weiss GM, Hirsh H, Kautz HA, Porter BW (2000) A quantitative study of small disjuncts. In: Kautz HA, Porter BW (eds) AAAI\/IAAI. AAAI Press \/ The MIT Press, New York, pp 665\u2013670"},{"key":"9144_CR51","doi-asserted-by":"crossref","unstructured":"Goder A, Filkov V (2008) Consensus clustering algorithms: comparison and refinement. In: Proceedings of the meeting on algorithm engineering & expermiments, Society for Industrial and Applied Mathematics, USA, pp 109-117","DOI":"10.1137\/1.9781611972887.11"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09144-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09144-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09144-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T09:33:03Z","timestamp":1730539983000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09144-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,21]]},"references-count":51,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["9144"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09144-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2023,11,21]]},"assertion":[{"value":"10 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}