Feature selection for label distribution learning using dual-similarity based neighborhood fuzzy entropy

被引:30
作者
Deng, Zhixuan [1 ,2 ]
Li, Tianrui [1 ,2 ]
Deng, Dayong [3 ]
Liu, Keyu [1 ,2 ]
Zhang, Pengfei [1 ,2 ]
Zhang, Shiming [1 ,2 ]
Luo, Zhipeng [1 ,2 ]
机构
[1] Southwest Jiaotong Univ, Sch Comp & Artificial Intelligence, Chengdu 611756, Peoples R China
[2] Mfg Ind Chains Collaborat & Informat Support Techn, Key Lab Sichuan Prov, Chengdu 611756, Peoples R China
[3] Zhejiang Normal Univ, Xingzhi Coll, Lanxi 321100, Peoples R China
基金
国家重点研发计划; 美国国家科学基金会;
关键词
Neighborhood rough sets; Fuzzy entropy; Feature selection; Label distribution learning; ATTRIBUTE REDUCTION; MUTUAL INFORMATION;
D O I
10.1016/j.ins.2022.10.054
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Label distribution learning (LDL) is a novel framework for handling label ambiguity problems and has been used widely in practice. However, dealing with high-dimensional data or data with redundant features in the LDL context is still an open problem. Existing feature selection algorithms cannot be directly applied to LDL due to the unique challenges caused by the label uncertainty nature. In this paper, we propose a novel LDL feature selection algorithm based on neighborhood rough sets. Specifically, we first introduce dualsimilarity that is used to measure sample similarity in both the feature and the label spaces. Second, we invent a novel neighborhood fuzzy entropy as a feature evaluation metric, with which neighborhood rough sets can be applied to deal with LDL problems. Lastly, we complete a feature selection model that inherits the spirit of neighborhood rough sets and neighborhood fuzzy entropy. Extensive experiments have been conducted on twelve real-world LDL datasets, and the results demonstrate the superiority of our proposed model against to other six state-of-the-art algorithms.(c) 2022 Elsevier Inc. All rights reserved.
引用
收藏
页码:385 / 404
页数:20
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