Optimization approach for feature selection in multi-label classification
- Authors
- Lim, Hyunki; Lee, Jaesung; Kim, Dae-Won
- Issue Date
- Apr-2017
- Publisher
- ELSEVIER SCIENCE BV
- Keywords
- Multi-label feature selection; Numerical optimization; Mutual information
- Citation
- PATTERN RECOGNITION LETTERS, v.89, pp 25 - 30
- Pages
- 6
- Journal Title
- PATTERN RECOGNITION LETTERS
- Volume
- 89
- Start Page
- 25
- End Page
- 30
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/4578
- DOI
- 10.1016/j.patrec.2017.02.004
- ISSN
- 0167-8655
1872-7344
- Abstract
- Nowadays, many data sources that include multi-label learning and multi-label classification have emerged in recent application areas. To achieve high classification accuracy, the multi-label feature selection method has received much attention because its accuracy can be significantly improved by selecting important features. In previous multi-label feature selection studies, a score function was designed based on the measure of the dependency between features and labels. However, identifying the optimal feature subset is an impractical task because all possible feature subsets are 2 N, where N is the number of total features in a given dataset. Thus, the conventional methods utilized a greedy search approach that can be stuck in local optima. To circumvent the drawback of the greedy approaches, we design a score function based on mutual information and present a numerical optimization approach to avoid being stuck in the local optima. The experimental results demonstrate the superiority of the proposed multi-label feature selection method. (C) 2017 Elsevier B.V. All rights reserved.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Software > Department of Artificial Intelligence > 1. Journal Articles
- 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/4578)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.