Fuzzy entropy semi-supervised support vector data description

Trung Le; Dat Tran; Tien Tran; Khanh Nguyen; Wanli Ma

doi:10.1109/IJCNN.2013.6707033

Fuzzy entropy semi-supervised support vector data description

Trung Le, Dat Tran, Tien Tran, Khanh Nguyen, Wanli Ma

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

5 Citations (Scopus)

Abstract

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 language	English
Title of host publication	The 2013 International Joint Conference on Neural Networks (IJCNN)
Place of Publication	Piscataway NJ USA
Publisher	Curran Associates, Inc.
Number of pages	5
ISBN (Electronic)	9781467361293
ISBN (Print)	9781467361286
DOIs	https://doi.org/10.1109/IJCNN.2013.6707033
Publication status	Published - 2013
Externally published	Yes
Event	IEEE International Joint Conference on Neural Networks 2013 - Dallas, United States of America Duration: 4 Aug 2013 → 9 Aug 2013 https://ieeexplore.ieee.org/xpl/conhome/6691896/proceeding (Proceedings)

Conference

Conference	IEEE International Joint Conference on Neural Networks 2013
Abbreviated title	IJCNN 2013
Country	United States of America
City	Dallas
Period	4/08/13 → 9/08/13
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6691896/proceeding (Proceedings)

Access to Document

10.1109/IJCNN.2013.6707033

Link to publication in Scopus

Cite this

@inproceedings{f8cf357d9ed946ac975dea5fb06db0b6,

title = "Fuzzy entropy semi-supervised support vector data description",

abstract = "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.",

author = "Trung Le and Dat Tran and Tien Tran and Khanh Nguyen and Wanli Ma",

year = "2013",

doi = "10.1109/IJCNN.2013.6707033",

language = "English",

isbn = "9781467361286",

booktitle = "The 2013 International Joint Conference on Neural Networks (IJCNN)",

publisher = "Curran Associates, Inc.",

note = "IEEE International Joint Conference on Neural Networks 2013, IJCNN 2013 ; Conference date: 04-08-2013 Through 09-08-2013",

url = "https://ieeexplore.ieee.org/xpl/conhome/6691896/proceeding",

}

Le, T, Tran, D, Tran, T, Nguyen, K & Ma, W 2013, Fuzzy entropy semi-supervised support vector data description. in The 2013 International Joint Conference on Neural Networks (IJCNN)., 6707033, Curran Associates, Inc., Piscataway NJ USA, IEEE International Joint Conference on Neural Networks 2013, Dallas, United States of America, 4/08/13. https://doi.org/10.1109/IJCNN.2013.6707033

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AU - Tran, Dat

AU - Tran, Tien

AU - Nguyen, Khanh

AU - Ma, Wanli

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Y1 - 2013

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AB - 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.

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