SCLS: Multi-label feature selection based on scalable criterion for large label set
- Authors
- Lee, Jaesung; Kim, Dae-Won
- Issue Date
- Jun-2017
- Publisher
- ELSEVIER SCI LTD
- Keywords
- Machine learning; Multi-label learning; Multi-label feature selection; Relevance evaluation; Conditional relevance
- Citation
- PATTERN RECOGNITION, v.66, pp 342 - 352
- Pages
- 11
- Journal Title
- PATTERN RECOGNITION
- Volume
- 66
- Start Page
- 342
- End Page
- 352
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/4348
- DOI
- 10.1016/j.patcog.2017.01.014
- ISSN
- 0031-3203
1873-5142
- Abstract
- Multi-label feature selection involves the selection of relevant features from multi-labeled datasets, resulting in a potential improvement of multi-label learning accuracy. In conventional multi-label feature selection methods, the final feature subset is obtained by identifying the features of high relevance with low redundancy. Thus, accurate score evaluation is a key factor for obtaining an effective feature subset. However, conventional methods suffer from inaccurate conditional relevance evaluation when a large number of labels are involved. As a result, irrelevant features can be a member of the final feature subset, leading to low multi-label learning accuracy. In this paper, we propose a new multi-label feature selection method. Using a scalable relevance evaluation process that evaluates conditional relevance more accurately, the proposed method significantly improves multi-label learning accuracy compared with conventional multi-label feature selection methods.
- 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/4348)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.