Feature-specific mutual information variation for multi-label feature selection

Madjarov, 2012, An extensive experimental comparison of methods for multi-label learning, Pattern Recogn., 45, 3084, 10.1016/j.patcog.2012.03.004 W. Gao, Z.-H. Zhou, On the consistency of multi-label learning, in: Proceedings of the 24th annual conference on learning theory, 2011, pp. 341–358. W. Gao, Y. Li, L. Hu, Multilabel feature selection with constrained latent structure shared term, IEEE Trans. Neural Networks Learn. Syst. B. Klimt, Y. Yang, The enron corpus: A new dataset for email classification research, in: European Conference on Machine Learning, Springer, 2004, pp. 217–226. Zhang, 2019, Distinguishing two types of labels for multi-label feature selection, Pattern Recogn., 95, 72, 10.1016/j.patcog.2019.06.004 Boutell, 2004, Learning multi-label scene classification, Pattern Recogn., 37, 1757, 10.1016/j.patcog.2004.03.009 Sun, 2013, Feature selection using dynamic weights for classification, Knowl.-Based Syst., 37, 541, 10.1016/j.knosys.2012.10.001 Sun, 2013, Selection of interdependent genes via dynamic relevance analysis for cancer diagnosis, J. Biomed. Inf., 46, 252, 10.1016/j.jbi.2012.10.004 Zhang, 2020, Multi-label feature selection based on high-order label correlation assumption, Entropy, 22, 797, 10.3390/e22070797 Hashemi, 2021, An efficient pareto-based feature selection algorithm for multi-label classification, Inf. Sci., 581, 428, 10.1016/j.ins.2021.09.052 Sun, 2021, Feature selection using fisher score and multilabel neighborhood rough sets for multilabel classification, Inf. Sci., 578, 887, 10.1016/j.ins.2021.08.032 Xiong, 2021, Feature selection based on label distribution and fuzzy mutual information, Inf. Sci., 574, 297, 10.1016/j.ins.2021.06.005 W. Gao, L. Hu, Y. Li, P. Zhang, Preserving similarity and staring decisis for feature selection, IEEE Trans. Artif. Intell. Lee, 2015, Fast multi-label feature selection based on information-theoretic feature ranking, Pattern Recogn., 48, 2761, 10.1016/j.patcog.2015.04.009 Gao, 2018, Class-specific mutual information variation for feature selection, Pattern Recogn., 79, 328, 10.1016/j.patcog.2018.02.020 Qian, 2022, Feature selection for label distribution learning via feature similarity and label correlation, Inf. Sci., 582, 38, 10.1016/j.ins.2021.08.076 Gao, 2018, Feature selection considering the composition of feature relevancy, Pattern Recogn. Lett., 112, 70, 10.1016/j.patrec.2018.06.005 Lal, 2006, Embedded methods, Feature extraction, Springer, 137, 10.1007/978-3-540-35488-8_6 Gao, 2018, Feature selection by integrating two groups of feature evaluation criteria, Expert Syst. Appl., 110, 11, 10.1016/j.eswa.2018.05.029 Huda, 2016, A hybrid feature selection with ensemble classification for imbalanced healthcare data: A case study for brain tumor diagnosis, IEEE Access, 4, 9145, 10.1109/ACCESS.2016.2647238 Lee, 2015, Mutual information-based multi-label feature selection using interaction information, Expert Syst. Appl., 42, 2013, 10.1016/j.eswa.2014.09.063 Lee, 2013, Feature selection for multi-label classification using multivariate mutual information, Pattern Recogn. Lett., 34, 349, 10.1016/j.patrec.2012.10.005 Lee, 2017, Scls: Multi-label feature selection based on scalable criterion for large label set, Pattern Recogn., 66, 342, 10.1016/j.patcog.2017.01.014 Zhang, 2021, Multi-label feature selection based on the division of label topics, Inf. Sci., 553, 129, 10.1016/j.ins.2020.12.036 Shannon, 2001, A mathematical theory of communication, ACM SIGMOBILE mobile computing and communications review, 5, 3, 10.1145/584091.584093 Spolaôr, 2016, A systematic review of multi-label feature selection and a new method based on label construction, Neurocomputing, 180, 3, 10.1016/j.neucom.2015.07.118 Xu, 2016, Multi-label learning with label-specific feature reduction, Knowl.-Based Syst., 104, 52, 10.1016/j.knosys.2016.04.012 Zhang, 2013, A review on multi-label learning algorithms, IEEE Trans. Knowl. Data Eng., 26, 1819, 10.1109/TKDE.2013.39 K. Trohidis, G. Tsoumakas, G. Kalliris, I.P. Vlahavas, Multi-label classification of music into emotions., in: ISMIR, Vol. 8, 2008, pp. 325–330. J. Read, A pruned problem transformation method for multi-label classification, in: Proc. 2008 New Zealand Computer Science Research Student Conference (NZCSRS 2008), vol. 143150, 2008, p. 41. G. Doquire, M. Verleysen, Feature selection for multi-label classification problems, in: International work-conference on artificial neural networks, Springer, 2011, pp. 9–16. Bania, 2020, R-ensembler: A greedy rough set based ensemble attribute selection algorithm with knn imputation for classification of medical data, Comput. Methods Programs Biomed., 184, 10.1016/j.cmpb.2019.105122 Bania, 2021, R-hefs: Rough set based heterogeneous ensemble feature selection method for medical data classification, Artif. Intell. Med., 114, 10.1016/j.artmed.2021.102049 Qian, 2020, Mutual information-based label distribution feature selection for multi-label learning, Knowl.-Based Syst., 195, 10.1016/j.knosys.2020.105684 Jian, 2016, Multi-label informed feature selection, IJCAI, 1627 Gonzalez-Lopez, 2020, Distributed multi-label feature selection using individual mutual information measures, Knowl.-Based Syst., 188, 10.1016/j.knosys.2019.105052 Liu, 2020, Feature selection for multi-label learning with streaming label, Neurocomputing, 387, 268, 10.1016/j.neucom.2020.01.005 Lin, 2017, Streaming feature selection for multilabel learning based on fuzzy mutual information, IEEE Trans. Fuzzy Syst., 25, 1491, 10.1109/TFUZZ.2017.2735947 Tsoumakas, 2011, Mulan: A java library for multi-label learning, J. Mach. Learn. Res., 12, 2411 Gao, 2020, Feature redundancy based on interaction information for multi-label feature selection, IEEE Access, 8, 146050, 10.1109/ACCESS.2020.3015755 Lee, 2015, Memetic feature selection algorithm for multi-label classification, Inf. Sci., 293, 80, 10.1016/j.ins.2014.09.020 Zhang, 2007, Ml-knn: A lazy learning approach to multi-label learning, Pattern Recogn., 40, 2038, 10.1016/j.patcog.2006.12.019 Al-Salemi, 2019, Multi-label arabic text categorization: A benchmark and baseline comparison of multi-label learning algorithms, Inf. Process. Manage., 56, 212, 10.1016/j.ipm.2018.09.008 Wu, 2017, A unified view of multi-label performance measures, International Conference on Machine Learning, PMLR, 3780 Sorower, 2010, A literature survey on algorithms for multi-label learning, Oregon State University, Corvallis, 18, 1