CC<i>k</i>EL: Compensation-based correlated <i>k</i>-labelsets for classifying imbalanced multi-label data

Author:

Xiao Qianpeng1,Shao Changbin12,Xu Sen3,Yang Xibei1,Yu Hualong1

Affiliation:

1. School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China

2. Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi, Jiangsu, China

3. School of Information Engineering, Yancheng Institute of Technology, Yancheng, Jiangsu, China

Abstract

<abstract> <p>Imbalanced data distribution and label correlation are two intrinsic characteristics of multi-label data. This occurs because in this type of data, instances associated with certain labels may be sparse, and some labels may be associated with others, posing a challenge for traditional machine learning techniques. To simultaneously adapt imbalanced data distribution and label correlation, this study proposed a novel algorithm called compensation-based correlated <italic>k</italic>-labelsets (CC<italic>k</italic>EL). First, for each label, the CC<italic>k</italic>EL selects the <italic>k</italic>-1 strongest correlated labels in the label space to constitute multiple correlated <italic>k</italic>-labelsets; this improves its efficiency in comparison with the random <italic>k</italic>-labelsets (RA<italic>k</italic>EL) algorithm. Then, the CC<italic>k</italic>EL transforms each <italic>k</italic>-labelset into a multiclass issue. Finally, it uses a fast decision output compensation strategy to address class imbalance in the decoded multi-label decision space. We compared the performance of the proposed CC<italic>k</italic>EL algorithm with that of multiple popular multi-label imbalance learning algorithms on 10 benchmark multi-label datasets, and the results show its effectiveness and superiority.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

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