A mixture of multiple linear classifiers with sample weight and manifold regularization

Weite Li, Benhui Chen, Bo Zhou, Jinglu Hu

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

A mixture of multiple linear classifiers is famous for its efficiency and effectiveness to tackle nonlinear classification problems. Each classifier contains one linear function multiplied with a gated function, which restricts its corresponding classifier to a local region. Previous researches mainly focus on the partition of local regions, since its quality directly determines the performance of mixture models. However, in real-world data sets, imbalanced and insufficient labeled data are two frequently encountered problems, which also have large influences on the performance of learned classifiers but are seldom considered or explored in the context of mixture models. In this paper, these missing components are introduced into the original formulation of mixture models, namely, a sample weighting scheme for imbalanced data distributions and a manifold regularization to leverage unlabeled data. Then, two solutions with closed form are provided for parameter optimization. Experimental results in the end of our paper exhibit the significance of the added components. As a result, a mixture of multiple linear classifiers can be extended to imbalanced and semi-supervised learning problems.

本文言語English
ホスト出版物のタイトル2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3747-3752
ページ数6
ISBN(電子版)9781509061815
DOI
出版ステータスPublished - 2017 6 30
イベント2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
継続期間: 2017 5 142017 5 19

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks
2017-May

Other

Other2017 International Joint Conference on Neural Networks, IJCNN 2017
国/地域United States
CityAnchorage
Period17/5/1417/5/19

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

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