Approximating dependency for efficient multi-label feature selection
- Authors
- Lim, H.; Lee, J.; Kim, D.-W.
- Issue Date
- 2015
- Publisher
- Springer Verlag
- Keywords
- Multi-label feature selection; Mutual information; Nyström method; Quadratic programming
- Citation
- Lecture Notes in Electrical Engineering, v.330, pp 245 - 250
- Pages
- 6
- Journal Title
- Lecture Notes in Electrical Engineering
- Volume
- 330
- Start Page
- 245
- End Page
- 250
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/56016
- DOI
- 10.1007/978-3-662-45402-2_36
- ISSN
- 1876-1100
1876-1119
- Abstract
- Multi-label feature selection is an important task that can be done before applying multi-label classification algorithms because the multi-label classification performance is naturally influenced by input features. To solve this problem, feature selection algorithm considers the dependency of each feature to labels as well as the dependency among features simultaneously. However, feature selection methods suffer from additional computational burden for calculating the dependency among features. In this paper, we propose an efficient feature selection algorithm extending quadratic programming feature selection for multi-label datasets and use the Nyström approximation. Experimental results demonstrated the proposed method reduces the computational cost for performing multi-label feature selection. © Springer-Verlag Berlin Heidelberg 2015.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/56016)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.