[1]HU Jun,WANG Haifeng.Feature selection algorithm of multi-labeled data based on weighted information granulation[J].CAAI Transactions on Intelligent Systems,2023,18(3):619-628.[doi:10.11992/tis.202111058]
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Feature selection algorithm of multi-labeled data based on weighted information granulation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 3 Page number: 619-628 Column: 学术论文—人工智能基础 Public date: 2023-07-05
Title:
Feature selection algorithm of multi-labeled data based on weighted information granulation
Author(s):
HU Jun1; 2;  WANG Haifeng1; 2
1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Keywords:
neighborhood rough set; information granulation; multi-label learning; label significance; label relationship; feature weight; feature selection; spectral clustering
CLC:
TP391
DOI:
10.11992/tis.202111058
Abstract:
Feature selection can remove irrelevant and redundant features. It is an efficient tool to solve the disaster of multi-labeled data dimensions. Existing multi-labeled feature selection algorithms did not take the correlation of label space into account, and considered that the relevant labels of each sample have the same importance, and ignored that the feature space may be the internal factor caused by the difference of label importance, so that the selected features can not accurately and comprehensively describe the samples and the calculation process is very complex. In this paper, the correlation between labels is used to divide the label space to simplify the calculation. Then, the label importance measure and feature weight are defined. And further, a feature selection algorithm of multi-label data based on weighted information granulation is proposed. The comparison and analysis on real multi-labeled data set of experiment show that the proposed algorithm is superior to other comparison algorithms in all evaluation indicators, which verifies effectiveness and feasibility of the algorithm.
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