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Multi-label optimal margin distribution machine.
- Source :
- Machine Learning; Mar2020, Vol. 109 Issue 3, p623-642, 20p
- Publication Year :
- 2020
-
Abstract
- Multi-label support vector machine (Rank-SVM) is a classic and effective algorithm for multi-label classification. The pivotal idea is to maximize the minimum margin of label pairs, which is extended from SVM. However, recent studies disclosed that maximizing the minimum margin does not necessarily lead to better generalization performance, and instead, it is more crucial to optimize the margin distribution. Inspired by this idea, in this paper, we first introduce margin distribution to multi-label learning and propose multi-label Optimal margin Distribution Machine (mlODM), which optimizes the margin mean and variance of all label pairs efficiently. Extensive experiments in multiple multi-label evaluation metrics illustrate that mlODM outperforms SVM-style multi-label methods. Moreover, empirical study presents the best margin distribution and verifies the fast convergence of our method. [ABSTRACT FROM AUTHOR]
- Subjects :
- SUPPORT vector machines
LABELS
CLASSIFICATION algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 08856125
- Volume :
- 109
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Machine Learning
- Publication Type :
- Academic Journal
- Accession number :
- 142186136
- Full Text :
- https://doi.org/10.1007/s10994-019-05837-8