@article{Xiao2024, 
author = {Qianpeng Xiao and Changbin Shao and Sen Xu and Xibei Yang and Hualong Yu},
title = {CCkEL: Compensation-based correlated k-labelsets for classifying imbalanced multi-label data},
year = {2024},
journal = {Electronic Research Archive},
volume = {32},
number = {5},
pages = {3038-3058},
keywords = {multi-label learning, class imbalance, random k-labelsets, label correlation, decision output compensation},
url = {https://www.sciopen.com/article/10.3934/era.2024139},
doi = {10.3934/era.2024139},
abstract = {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 k-labelsets (CCkEL). First, for each label, the CCkEL selects the k-1 strongest correlated labels in the label space to constitute multiple correlated k-labelsets; this improves its efficiency in comparison with the random k-labelsets (RAkEL) algorithm. Then, the CCkEL transforms each k-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 CCkEL 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.}
}