Feature selection for multi-label classification using multivariate mutual information
- Authors
- Lee, Jaesung; Kim, Dae-Won
- Issue Date
- Feb-2013
- Publisher
- ELSEVIER SCIENCE BV
- Keywords
- Multi-label feature selection; Multivariate feature selection; Multivariate mutual information; Label dependency
- Citation
- PATTERN RECOGNITION LETTERS, v.34, no.3, pp 349 - 357
- Pages
- 9
- Journal Title
- PATTERN RECOGNITION LETTERS
- Volume
- 34
- Number
- 3
- Start Page
- 349
- End Page
- 357
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/14855
- DOI
- 10.1016/j.patrec.2012.10.005
- ISSN
- 0167-8655
1872-7344
- Abstract
- Recently, classification tasks that naturally emerge in multi-label domains, such as text categorization, automatic scene annotation, and gene function prediction, have attracted great interest. As in traditional single-label classification, feature selection plays an important role in multi-label classification. However, recent feature selection methods require preprocessing steps that transform the label set into a single label, resulting in subsequent additional problems. In this paper, we propose a feature selection method for multi-label classification that naturally derives from mutual information between selected features and the label set. The proposed method was applied to several multi-label classification problems and compared with conventional methods. The experimental results demonstrate that the proposed method improves the classification performance to a great extent and has proved to be a useful method in selecting features for multi-label classification problems. (C) 2012 Elsevier B.V. All rights reserved.
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- Appears in
Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
- College of Software > Department of Artificial Intelligence > 1. Journal Articles
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