Multi-Label Classification: Inconsistency and Class Balanced K-Nearest Neighbor
Hua Wang, Chris Ding, Heng Huang.
AAAI - 2010
Many existing approaches employ one-vs-rest method to decompose a multi-label classification problem into a set of 2class classification problems, one for each class. This method is valid in traditional single-label classification, it, however, incurs training inconsistency in multi-label classification, because in the latter a data point could belong to more than one class. In order to deal with this problem, in this work, we further develop classical K-Nearest Neighbor classifier and propose a novel Class Balanced K-Nearest Neighbor approach for multi-label classification by emphasizing balanced usage of data from all the classes. In addition, we also propose a Class Balanced Linear Discriminant Analysis approach to address high-dimensional multi-label input data. Promising experimental results on three broadly used multi-label data sets demonstrate the effectiveness of our approach.
Links
- View publications from Hua Wang
- View publications presented in AAAI
- View publications researching Multiple-Label Learning
Cite this paper
MLA
Wang, Hua, Chris Ding, and Heng Huang. "Multi-label classification: Inconsistency and class balanced k-nearest neighbor." Twenty-Fourth AAAI Conference on Artificial Intelligence. 2010.
BibTeX
@inproceedings{wang2010multi,
title={Multi-label classification: Inconsistency and class balanced k-nearest neighbor},
author={Wang, Hua and Ding, Chris and Huang, Heng},
booktitle={Twenty-Fourth AAAI Conference on Artificial Intelligence},
year={2010}
}