One-Class Classification Using Quasi-Linear Support Vector Machine

Peifeng Liang, Weite Li, Yudong Wang, Takayuki Furuzuki

研究成果: Conference contribution

1 引用 (Scopus)

抜粋

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.

元の言語English
ホスト出版物のタイトルProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
出版者Institute of Electrical and Electronics Engineers Inc.
ページ662-667
ページ数6
ISBN(電子版)9781538666500
DOI
出版物ステータス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
Japan
Miyazaki
期間18/10/718/10/10

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management
  • Health Informatics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Human-Computer Interaction

フィンガープリント One-Class Classification Using Quasi-Linear Support Vector Machine' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

    Liang, P., Li, W., Wang, Y., & Furuzuki, T. (2019). One-Class Classification Using Quasi-Linear Support Vector Machine. : Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 662-667). [8616117] (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2018.00121