A new Feature Reduction Algorithm Based on Fuzzy Rough Relation for the Multi-label Classification

Abstract

The paper aims to improve the multi-label classification performance using the feature reduction technique. According to the determination of the dependency among features based on fuzzy rough relation, features with the highest dependency score will be retained in the reduction set. The set is subsequently applied to enhance the performance of the multi-label classifier. We investigate the effectiveness of the proposed model againts the baseline via time complexity. Keywords: Fuzzy rough relation, label-specific feature, feature reduction set References [1] Richard Jensen, Chris Cornelis, Fuzzy-Rough Nearest Neighbor Classification and Prediction. Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing, 2011, 310-319. [2] Y.H. Qian, Q. Wang, H.H. Cheng, J.Y. Liang, C.Y. Dang, Fuzzy-Rough feature selection accelerator, Fuzzy Sets Syst. 258 (2014) 61-78. [3] Quang-Thuy Ha, Thi-Ngan Pham, Van-Quang Nguyen, Minh-Chau Nguyen, Thanh-Huyen Pham, Tri-Thanh Nguyen, A New Text Semi-supervised Multi-label Learning Model Based on Using the Label-Feature Relations, International Conference on Computational Collective Intelligence, LNAI 11055, Springer, 2018, pp. 403-413. [4] Daniel Kostrzewa, Robert Brzeski, The data Dimensionality Reduction and Feature Weighting in the Classification Process Using Forest Optimization Algorithm, ACIIDS, 2019, pp. 97-108. [5] Nele Verbiest, Fuzzy Rough and Evolutionary Approaches to Instance Selection, PhD Thesis, Ghent University, 2014. [6] Y. Yu, W. Pedrycz, D.Q. Miao, Multi-label classification by exploiting label correlations, Expert syst, Appl. 41 (2014) 2989-3004. [7] M.L. Zhang, LIFT: Multi-label learning with label-specific features, IEEE Trans, Pattern Anal, Mach, Intell 37 (2015) 107-120. [8] Suping Xu, Xibei Yang, Hualong Yu, Dong-Jun Yu, Jingyu Yang, Eric CC Tsang, Multi-label learning with label-specific feature reduction, Knowledge-Based Systems 104 (2016) 52-61. https://doi.org/10.1080/24751839.2017.1364925. [9] Thi-Ngan Pham, Van-Quang Nguyen, Van-Hien Tran, Tri-Thanh Nguyen, Quang-Thuy Ha, A Semi-supervised multi-label classification framework with feature reduction and enrichment, Journal of Information and Telecommunication 1(4) (2017) 305-318. [10] M. Ghaemi, M.R. Feizi-Derakhshi, Feature selection using forest optimization algorithm, Pattern Recognition 60 (2016) 121-129. [11] M.L. Zhang, Z.H. Zhou, ML-KNN: A lazy learning approach to multi-label learning, Pattern Recognition 40 (2007) 2038-2048. [12] M.Z. Ahmad, M.K. Hasan, A New Approach for Computing Zadeh's Extension Principle, MATEMATIKA. 26(1) (2010) 71-81. [13] Richard Jensen, Neil Mac Parthaláin and Qiang Shen. Fuzzy-rough data mining (using the Weka data mining suite), A Tutorial, IEEE WCCI 2014, Beijing, China, July 6, 2014. [14] D. Dubois, H. Prade, Rough fuzzy sets and fuzzy rough sets, Int. J. Gen. Syst. 17 (1990) 191-209.