Multi-Label Streaming Feature Selection via Class-Imbalance Aware Rough Set

被引:2
|
作者
Zou, Yizhang [1 ,2 ]
Hu, Xuegang [1 ,2 ,3 ]
Li, Peipei [1 ,2 ]
Li, Junlong [1 ,2 ]
机构
[1] Hefei Univ Technol, Minist Educ, Key Lab Knowledge Engn Big Data, Hefei 230009, Anhui, Peoples R China
[2] Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Anhui, Peoples R China
[3] Anhui Prov Key Lab Ind Safety & Emergency Technol, Hefei 230009, Anhui, Peoples R China
来源
2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2021年
关键词
Multi-label learning; Streaming feature selection; Class imbalance; Rough set;
D O I
10.1109/IJCNN52387.2021.9533614
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Multi-label feature selection aims to select discriminative attributes in multi-label scenario, but most of existing multi-label feature selection methods fail to consider streaming features, i.e. features gradually flow one by one, which is more common in real-world applications. In addition, though there are already some representative works on multi-label streaming feature selection, they fail to tackle the class-imbalance problem, which exists widely in multi-label learning. In fact, classimbalance will lead to the performance degradation of multi-label learning models. Thus considering class-imbalance problem in multi-label scenario is beneficial to multi-label feature selection because more precise feature evaluation is achieved. Motivated by this, we propose a new rough set named as class-imbalance aware rough set model which can fit class-imbalance problem well. To address streaming features, we construct a novel streaming feature selection framework called SFSCI(Streaming Feature Selection via Class-Imbalance aware rough set), which contains online irrelevancy discarding and online redundancy reduction. Finally, an empirical study on a series of benchmark data sets demonstrates that the proposed method is superior to other state-of-the-art multi-label feature selection methods, including several multi-label streaming feature selection methods.
引用
收藏
页数:9
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