Fuzzy Entropy Semi-Supervised Support Vector Data Description

Trung Minh LE, Dat TRAN, Tien Tran, Khanh Nyugen, Wanli MA

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

5 Citations (Scopus)


Support Vector Data Description (SVDD) is known as one of the best kernel-based methods for one-class classification problems. SVDD requires fully labelled data sets. However, in reality, an abundant amount of data can be easily collected, while the labelling process is often expensive, time-consuming, and error-prone. Therefore, partially labelled data sets are popular and easy to obtain. In this paper, we propose a semi-supervised learning method, Fuzzy Entropy Semi-supervised SVDD (FS3VDD), to extend SVDD to cope with partially labelled data sets. The learning model employs fuzzy membership and fuzzy entropy to help the labelling of the unlabeled data.
Original languageEnglish
Title of host publicationThe 2013 International Joint Conference on Neural Networks (IJCNN)
EditorsPlaman Angelov, Daniel Levine
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Print)9781467361293
Publication statusPublished - 2013
Event2013 International Joint Conference on Neural Networks (IJCNN) - Dallas, Texas, United States
Duration: 4 Aug 20139 Aug 2013


Conference2013 International Joint Conference on Neural Networks (IJCNN)
Country/TerritoryUnited States


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