An efficient Pareto-based feature selection algorithm for multi-label classification

Guan, 2021, A differential evolution based feature combination selection algorithm for high-dimensional data, Inf. Sci. (Ny), 547, 870, 10.1016/j.ins.2020.08.081 Miao, 2016, A Survey on Feature Selection, in, Procedia Comput. Sci., 919, 10.1016/j.procs.2016.07.111 Dowlatshahi, 2018, A novel three-stage filter-wrapper framework for miRNA subset selection in cancer classification, Informatics., 5, 13, 10.3390/informatics5010013 Reyes, 2015, Scalable extensions of the ReliefF algorithm for weighting and selecting features on the multi-label learning context, Neurocomputing., 161, 168, 10.1016/j.neucom.2015.02.045 Pereira, 2018, Categorizing feature selection methods for multi-label classification, Artif. Intell. Rev., 49, 57, 10.1007/s10462-016-9516-4 Li, 2018, Feature Selection: A Data Perspective, ACM Comput. Surv., 50, 1, 10.1145/3136625 Li, 2015, Bi-goal evolution for many-objective optimization problems, Artif. Intell., 228, 45, 10.1016/j.artint.2015.06.007 Bayati, 2020, 25th Int. Comput. Conf. Comput, Soc. Iran, IEEE, 2020, 1 Dowlatshahi, 2020, Fuzzy particle swarm optimization with nearest-better neighborhood for multimodal optimization, Iran. J. Fuzzy Syst., 17, 7 Dowlatshahi, 2019, Winner Determination in Combinatorial Auctions using Hybrid Ant Colony Optimization and Multi-Neighborhood Local Search, J. AI Data Min., 5, 169 Dowlatshahi, 2014, GGSA: A Grouping Gravitational Search Algorithm for data clustering, Eng. Appl. Artif. Intell., 36, 114, 10.1016/j.engappai.2014.07.016 Li, 2020, Multi-objective feature selection using hybridization of a genetic algorithm and direct multisearch for key quality characteristic selection, Inf. Sci. (Ny), 523, 245, 10.1016/j.ins.2020.03.032 Taradeh, 2019, An evolutionary gravitational search-based feature selection, Inf. Sci. (Ny), 497, 219, 10.1016/j.ins.2019.05.038 Hashemi, 2021, A pareto-based ensemble of feature selection algorithms, Expert Syst. Appl., 180, 115130, 10.1016/j.eswa.2021.115130 Zhou, 2020, Many-objective optimization of feature selection based on two-level particle cooperation, Inf. Sci. (Ny), 532, 91, 10.1016/j.ins.2020.05.004 Hashemi, 2020, Multi-label feature selection using multi-criteria decision making, Knowledge-Based Syst., 10.1016/j.knosys.2020.106365 Hashemi, 2021, A VIKOR-based multi-target feature selection, Expert Syst. Appl., 10.1016/j.eswa.2021.115224 Hashemi, 2021, Ensemble of feature selection algorithms: a multi-criteria decision-making approach, Int. J. Mach. Learn. Cybern. Zhou, 2021, A problem-specific non-dominated sorting genetic algorithm for supervised feature selection, Inf. Sci. (Ny), 547, 841, 10.1016/j.ins.2020.08.083 Kashef, 2019, A label-specific multi-label feature selection algorithm based on the Pareto dominance concept, Pattern Recognit., 88, 654, 10.1016/j.patcog.2018.12.020 Von Lücken, 2014, A survey on multi-objective evolutionary algorithms for many-objective problems, Comput. Optim. Appl., 58, 707 Yuan, 2016, A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization, IEEE Trans. Evol. Comput., 20, 16, 10.1109/TEVC.2015.2420112 Hashemi, 2020, MGFS: A multi-label graph-based feature selection algorithm via PageRank centrality, Expert Syst. Appl., 142, 113024, 10.1016/j.eswa.2019.113024 Kashef, 2018, Multilabel feature selection: A comprehensive review and guiding experiments, Wiley Interdiscip, Rev. Data Min. Knowl. Discov., 8 Asilian Bidgoli, 2021, Reference-point-based multi-objective optimization algorithm with opposition-based voting scheme for multi-label feature selection, Inf. Sci. (Ny)., 547, 1, 10.1016/j.ins.2020.08.004 Dong, 2020, A many-objective feature selection for multi-label classification, Knowledge-Based Syst., 208, 106456, 10.1016/j.knosys.2020.106456 Dai, 2020, Novel multi-label feature selection via label symmetric uncertainty correlation learning and feature redundancy evaluation, Knowledge-Based Syst., 207, 106342, 10.1016/j.knosys.2020.106342 Sun, 2020, Multilabel feature selection using ML-ReliefF and neighborhood mutual information for multilabel neighborhood decision systems, Inf. Sci. (Ny), 537, 401, 10.1016/j.ins.2020.05.102 Zhang, 2020, Binary differential evolution with self-learning for multi-objective feature selection, Inf. Sci. (Ny), 507, 67, 10.1016/j.ins.2019.08.040 Paniri, 2019, A multi-label feature selection algorithm based on ant colony optimization, Knowledge-Based Syst. Hashemi, 2021, A bipartite matching-based feature selection for multi-label learning, Int. J. Mach. Learn. Cybern., 12, 459, 10.1007/s13042-020-01180-w Che, 2020, A novel approach for learning label correlation with application to feature selection of multi-label data, Inf. Sci. (Ny), 512, 795, 10.1016/j.ins.2019.10.022 Paniri, 2021, Ant-TD: Ant colony optimization plus temporal difference reinforcement learning for multi-label feature selection, Swarm Evol. Comput., 64, 100892, 10.1016/j.swevo.2021.100892 Zhang, 2021, Multi-label feature selection based on the division of label topics, Inf. Sci. (Ny), 553, 129, 10.1016/j.ins.2020.12.036 Paul, 2021, Multi-objective PSO based online feature selection for multi-label classification, Knowledge-Based Syst., 222, 106966, 10.1016/j.knosys.2021.106966 Fan, 2021, Multi-label feature selection with constraint regression and adaptive spectral graph, Knowledge-Based Syst., 212, 106621, 10.1016/j.knosys.2020.106621 Hoerl, 1970, Ridge Regression: Applications to Nonorthogonal Problems, Technometrics., 12, 69, 10.1080/00401706.1970.10488635 S.R. McCurdy, Ridge regression and provable deterministic ridge leverage score sampling, in: Adv. Neural Inf. Process. Syst., 2018: pp. 2463–2472. Talbi, 2009, From Design to Implementation C.R. Raquel, P.C. Naval, An effective use of crowding distance in multiobjective particle swarm optimization, in: GECCO 2005 - Genet. Evol. Comput. Conf., 2005: pp. 257–264. https://doi.org/10.1145/1068009.1068047. Xu, 2016, Improving evolutionary algorithm performance for integer type multi-objective building system design optimization, Energy Build., 127, 714, 10.1016/j.enbuild.2016.06.043 Zhang, 2014, Multi-label Attribute Evaluation Based on Fuzzy Rough Sets, in, 100 Cherman, 2015, Lazy Multi-label Learning Algorithms Based on Mutuality Strategies, J. Intell. Robot. Syst., 80, 261, 10.1007/s10846-014-0144-4 Zhang, 2019, Distinguishing two types of labels for multi-label feature selection, Pattern Recognit., 95, 72, 10.1016/j.patcog.2019.06.004 Zhang, 2019, Manifold regularized discriminative feature selection for multi-label learning, Pattern Recognit., 95, 136, 10.1016/j.patcog.2019.06.003 Huang, 2018, Manifold-based constraint Laplacian score for multi-label feature selection, Pattern Recognit. Lett., 112, 346, 10.1016/j.patrec.2018.08.021 Zhang, 2007, ML-KNN: A lazy learning approach to multi-label leaming, Pattern Recognit., 40, 2038, 10.1016/j.patcog.2006.12.019 Hastie, 2017, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Math. Intell. Coakley, 2000, Practical Nonparametric Statistics, J. Am. Stat. Assoc., 95, 332, 10.2307/2669565