Label correlation in multi-label classification using local attribute reductions with fuzzy rough sets

Che Xiaoya; Chen Degang; Mi Jusheng

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Label correlation in multi-label classification using local attribute reductions with fuzzy rough sets

机译：Label correlation in multi-label classification using local attribute reductions with fuzzy rough sets

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摘要

To improve the efficiency and effectiveness of multi-label learning tasks, label correlation has been explored and widely applied in multi-label classification. However, the existing common way to obtain label correlation is to count the co-occurrence frequency of labels in output space. From our perspective, the feature variables of input space also contain the basic information, which can affect each label variable in output space. Obviously, the crucial features for label can efficiently reflect the internal relationship between input space and the label variable. The high consistency of the crucial features for different labels indicates that these labels are closely related. Inspired by the above intuitive motivations, the crucial features explicitly for every local label class are defined and calculated based on local attribute reductions with fuzzy rough sets. Local label correlations are naturally constructed, and then global label correlation is obtained by aggregating related local label correlations. According to different judgment parameters alpha and global label correlation, the labels can be transformed into several independent label-related subsets. To search for the indispensable and representative features of each label-related subset, the local-reduction-based feature selection method (LRFS-alpha) is designed. Comprehensive experimental results on fifteen multi-label datasets characterize the performance of our methods against other seven multi-label learning methods. (c) 2021 Elsevier B.V. All rights reserved.

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《Fuzzy sets and systems》 |2022年第15期|121-144|共24页
作者
Che Xiaoya; Chen Degang; Mi Jusheng;
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作者单位

North China Elect Power Univ, Sch Math & Phys, Beijing 102206, Peoples R China;

Hebei Normal Univ, Coll Math & Informat Sci, Shijiazhuang 050016, Hebei, Peoples R China;

North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类
关键词
Multi-label classification; Label correlation; Feature selection; Local attribute reduction; Fuzzy rough sets;

Label correlation in multi-label classification using local attribute reductions with fuzzy rough sets

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