Feature Selection Algorithm Based on Label Correlation
LÜ Yuejiao1, LI Deyu1,2
1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006 2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006
Abstract In multi-label classification, each sample can be associated with multiple label classes at one time and some of them are related to each other. The classification performance is optimized by taking full advantage of these label correlations. Therefore, frequent itemsets are employed to mine the correlation between labels, and an improved multi-label feature selection algorithm is proposed for the multi-label attribute reduction algorithm based on neighborhood rough set. Then, the samples are further clustered and grouped according to the similarity of the features, and attribute reduction and classification are performed based on the label correlations in local samples. Finally, the effectiveness of the proposed algorithm is verified by experiments on 5 multi-label datasets.
Fund:Supported by National Natural Science Foundation of China(No.61672331), Key Research and Development Program of Shanxi Province(No.201803D421024, 201903D421041), Graduate Education Innovation Program of Shanxi Province(No.2019SY005)
Corresponding Authors:
LI Deyu, Ph.D., professor. His research interests include gra-nular computing and machine learning.
About author:: LÜ Yuejiao, master student. Her research interests include rough sets and multi-label learning.
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2020, Vol. 33 
