TOPSIS-ACO based feature selection for multi-label classification

References

• Moyano JM. Multi-label classification models for heterogeneous data: an ensemble-based approach [PhD thesis]. 2020.
   Google Scholar
• Paniri M, Dowlatshahi MB, Nezamabadi-Pour H. MLACO: a multi-label feature selection algorithm based on ant colony optimization. Knowl Based Syst. 2020;192:Article ID 105285. doi: 10.1016/j.knosys.2019.105285
   Web of Science ®Google Scholar
• Reyes O, Morell C, Ventura S. Scalable extensions of the relieff algorithm for weighting and selecting features on the multi-label learning context. Neurocomputing. 2015;161:168–182. doi: 10.1016/j.neucom.2015.02.045
   Web of Science ®Google Scholar
• Lee J, Kim D-W. Mutual information-based multi-label feature selection using interaction information. Expert Syst Appl. 2015;42(4):2013–2025. doi: 10.1016/j.eswa.2014.09.063
   Web of Science ®Google Scholar
• Bishop CM. Neural Networks for Pattern Recognition. Clarendon Press google schola. 1995;2:223–228.
   Google Scholar
• Kashef S, Nezamabadi-pour H, Nikpour B. Multilabel feature selection: a comprehensive review and guiding experiments. Wiley Interdiscip Rev: Data Min Knowl Discov. 2018;8(2):Article ID e1240.
   Web of Science ®Google Scholar
• Xu J, Shen K, Sun L. Multi-label feature selection based on fuzzy neighborhood rough sets. Complex Intell Syst. 2022;8:2105–2129. doi: 10.1007/s40747-021-00636-y
   Web of Science ®Google Scholar
• Lee J, Kim D-W. Memetic feature selection algorithm for multi-label classification. Inf Sci. 2015;293:80–96. doi: 10.1016/j.ins.2014.09.020
   Web of Science ®Google Scholar
• Jadhav S, He H, Jenkins K. Information gain directed genetic algorithm wrapper feature selection for credit rating. Appl Soft Comput. 2018;69:541–553. doi: 10.1016/j.asoc.2018.04.033
   Web of Science ®Google Scholar
• Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput. 2002;6(2):182–197. doi: 10.1109/4235.996017
   Web of Science ®Google Scholar
• Paul D, Jain A, Saha S, et al. Multi-objective PSO based online feature selection for multi-label classification. Knowl Based Syst. 2021;222:Article ID 106966. doi: 10.1016/j.knosys.2021.106966
   Web of Science ®Google Scholar
• Ikram ST, Priya V, Anbarasu B, et al. Prediction of iiot traffic using a modified whale optimization approach integrated with random forest classifier. J Supercomput. 2022;78(8):10725–10756. doi: 10.1007/s11227-021-04284-4
   Web of Science ®Google Scholar
• Samriya JK, Tiwari R, Cheng X, et al. Network intrusion detection using aco-dnn model with dvfs based energy optimization in cloud framework. Sustain Comput: Inform Syst. 2022;35:Article ID 100746.
   Web of Science ®Google Scholar
• Hashemi A, Dowlatshahi MB, Nezamabadi-Pour H. MGFS: a multi-label graph-based feature selection algorithm via pagerank centrality. Expert Syst Appl. 2020;142:Article ID 113024. doi: 10.1016/j.eswa.2019.113024
   Web of Science ®Google Scholar
• Sun L, Chen Y, Ding W, et al. AMFSA: adaptive fuzzy neighborhood-based multilabel feature selection with ant colony optimization. Appl Soft Comput. 2023;138:Article ID 110211. doi: 10.1016/j.asoc.2023.110211
   Web of Science ®Google Scholar
• Zhong H, Zhang P, Liu G. Multi-label feature selection via redundancy of the selected feature set. Appl Intell. 2022;53:11073–11091. doi: 10.1007/s10489-022-03365-y
   Web of Science ®Google Scholar
• Sun L, Yin T, Ding W, et al. Multilabel feature selection using ml-relieff and neighborhood mutual information for multilabel neighborhood decision systems. Inf Sci. 2020;537:401–424. doi: 10.1016/j.ins.2020.05.102
   Web of Science ®Google Scholar
• Chaudhuri A, Sahu TP. PROMETHEE-based hybrid feature selection technique for high-dimensional biomedical data: application to parkinson's disease classification. Electron Lett. 2020;56(25):1403–1406. doi: 10.1049/ell2.v56.25
   Web of Science ®Google Scholar
• Chaudhuri A, Sahu TP. A hybrid feature selection method based on binary Jaya algorithm for micro-array data classification. Comput Electr Eng. 2021;90:Article ID 106963. doi: 10.1016/j.compeleceng.2020.106963
   PubMed Web of Science ®Google Scholar
• Hashemi A, Dowlatshahi MB, Nezamabadi-Pour H. MFS-MCDM: multi-label feature selection using multi-criteria decision making. Knowl Based Syst. 2020;206:Article ID 106365. doi: 10.1016/j.knosys.2020.106365
   Web of Science ®Google Scholar
• Hashemi A, Dowlatshahi MB, Nezamabadi-pour H. VMFS: a VIKOR-based multi-target feature selection. Expert Syst Appl. 2021;182:Article ID 115224. doi: 10.1016/j.eswa.2021.115224
   Web of Science ®Google Scholar
• Bayati H, Dowlatshahi MB, Paniri M. MLPSO: a filter multi-label feature selection based on particle swarm optimization. In: 2020 25th International Computer Conference, Computer Society of Iran (CSICC). IEEE; 2020. p. 1–6.
   Google Scholar
• Desai J, Nguyen BH, Xue B. Multi-label feature selection using particle swarm optimization: novel initialization mechanisms. In: Australasian Joint Conference on Artificial Intelligence. Springer; 2019. p. 510–522.
   Google Scholar
• Paul D, Kumar R, Saha S, et al. Multi-objective cuckoo search-based streaming feature selection for multi-label dataset. ACM Trans Knowl Discov Data (TKDD). 2021;15(6):1–24. doi: 10.1145/3447586
   Web of Science ®Google Scholar
• Nematzadeh H, Enayatifar R, Mahmud M, et al. Frequency based feature selection method using whale algorithm. Genomics. 2019;111(6):1946–1955. doi: 10.1016/j.ygeno.2019.01.006
   PubMed Web of Science ®Google Scholar
• Paniri M, Dowlatshahi MB, Nezamabadi-pour H. Ant-TD: ant colony optimization plus temporal difference reinforcement learning for multi-label feature selection. Swarm Evol Comput. 2021;64:Article ID 100892. doi: 10.1016/j.swevo.2021.100892
   Web of Science ®Google Scholar
• Rostami M, Berahmand K, Nasiri E, et al. Review of swarm intelligence-based feature selection methods. Eng Appl Artif Intell. 2021;100:Article ID 104210. doi: 10.1016/j.engappai.2021.104210
   PubMed Web of Science ®Google Scholar
• Kou G, Lu Y, Peng Y, et al. Evaluation of classification algorithms using MCDM and rank correlation. Int J Inf Technol Decis Mak. 2012;11(01):197–225. doi: 10.1142/S0219622012500095
   Google Scholar
• Hwang CL, Yoon K. Multi-objective decision making–methods and application. A state-of-the-art study; 1981.
   Google Scholar
• Velasquez M, Hester PT. An analysis of multi-criteria decision making methods. Int J Oper Res. 2013;10(2):56–66.
   Google Scholar
• McDonald GC. Ridge regression. Wiley Interdiscip Rev Comput Stat. 2009;1(1):93–100. doi: 10.1002/wics.v1:1
   Google Scholar
• Cables E, Garcıa-Cascales MS, Lamata MT. The LTOPSIS: an alternative to topsis decision-making approach for linguistic variables. Expert Syst Appl. 2012;39(2):2119–2126. doi: 10.1016/j.eswa.2011.07.119
   Web of Science ®Google Scholar
• Chen T-Y. The inclusion-based TOPSIS method with interval-valued intuitionistic fuzzy sets for multiple criteria group decision making. Appl Soft Comput. 2015;26:57–73. doi: 10.1016/j.asoc.2014.09.015
   Web of Science ®Google Scholar
• Chen Y, Li KW, Liu S-F. An OWA-TOPSIS method for multiple criteria decision analysis. Expert Syst Appl. 2011;38(5):5205–5211. doi: 10.1016/j.eswa.2010.10.039
   Web of Science ®Google Scholar
• Kuo T. A modified TOPSIS with a different ranking index. Eur J Oper Res. 2017;260(1):152–160. doi: 10.1016/j.ejor.2016.11.052
   Web of Science ®Google Scholar
• Kashef S, Nezamabadi-pour H. An advanced ACO algorithm for feature subset selection. Neurocomputing. 2015;147:271–279. doi: 10.1016/j.neucom.2014.06.067
   Web of Science ®Google Scholar
• Saranya C, Manikandan G. A study on normalization techniques for privacy preserving data mining. Int J Eng Technol (IJET). 2013;5(3):2701–2704.
   Google Scholar
• Niwattanakul S, Singthongchai J, Naenudorn E, et al. Using of Jaccard coefficient for keywords similarity. In: Proceedings of the international multiconference of engineers and computer scientists. Vol. 1; 2013. p. 380–384.
   Google Scholar
• Zhang M-L, Zhou Z-H. ML-KNN: a lazy learning approach to multi-label learning. Pattern Recognit. 2007;40(7):2038–2048. doi: 10.1016/j.patcog.2006.12.019
   Web of Science ®Google Scholar
• Lee J, Kim D-W. Fast multi-label feature selection based on information-theoretic feature ranking. Pattern Recognit. 2015;48(9):2761–2771. doi: 10.1016/j.patcog.2015.04.009
   Web of Science ®Google Scholar
• Cai Y, Yang M, Yin H. Relieff-based multi-label feature selection. Int J Database Theory Appl. 2015;8(4):307–318. doi: 10.14257/ijdta
   Google Scholar
• Chen W, Yan J, Zhang B, et al. Document transformation for multi-label feature selection in text categorization. In: Seventh IEEE International Conference on Data Mining (ICDM 2007). IEEE; 2007. p. 451–456.
   Google Scholar
• Read J. A pruned problem transformation method for multi-label classification. In: Proceedings 2008 New Zealand Computer Science Research Student Conference (NZCSRS 2008). Vol. 143150; 2008. p. 41.
   Google Scholar
• Hashemi A, Dowlatshahi MB, Nezamabadi-pour H. An efficient pareto-based feature selection algorithm for multi-label classification. Inf Sci. 2021;581:428–447. doi: 10.1016/j.ins.2021.09.052
   Web of Science ®Google Scholar
• Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction. Vol. 2. Springer; 2009.
   Google Scholar