One-Class Classification Using Quasi-Linear Support Vector Machine

Peifeng Liang, Weite Li, Yudong Wang, Jinglu Hu

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

This paper proposes a novel method for one-class classification by using support vector machine (SVM) based on a divide-and-conquer strategy. An s% winner-take-all autoencoder is applied to realize a sophisticated partitioning which divides the dataset into many clusters. For each cluster, data points are separated from the origin in the feature space like a traditional one-class SVM (OCSVM). By designing a gated linear network, and generating the gate signal from the autoencoder, the proposed OCSVM is implemented in an exact same way as a standard OCSVM with a quasi-linear kernel composed by using a base kernel with the gate signals. Comparing to a traditional OCSVM, the proposed quasi-linear OCSVM is expected to capture a more compact region in the input space. The compact region will decrease the probability of outlier objects falling inside the domain of classifier, which give a better performance. The proposed quasi-linear OCSVM method is applied to different real-world datasets, and simulation results confirm the effectiveness of the proposed method.

本文言語English
ホスト出版物のタイトルProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ662-667
ページ数6
ISBN(電子版)9781538666500
DOI
出版ステータスPublished - 2019 1 16
イベント2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan
継続期間: 2018 10 72018 10 10

出版物シリーズ

名前Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conference

Conference2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
国/地域Japan
CityMiyazaki
Period18/10/718/10/10

ASJC Scopus subject areas

  • 情報システム
  • 情報システムおよび情報管理
  • 健康情報学
  • 人工知能
  • コンピュータ ネットワークおよび通信
  • 人間とコンピュータの相互作用

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