Fuzzy Entropy Semi-Supervised Support Vector Data Description

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

doi:10.1109/IJCNN.2013.6707033

Fuzzy Entropy Semi-Supervised Support Vector Data Description

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

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › 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)
Editors	Plaman Angelov, Daniel Levine
Place of Publication	USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	2339-2343
Number of pages	5
Volume	1
ISBN (Print)	9781467361293
DOIs	https://doi.org/10.1109/IJCNN.2013.6707033
Publication status	Published - 2013
Event	2013 International Joint Conference on Neural Networks (IJCNN) - Dallas, Texas, United States Duration: 4 Aug 2013 → 9 Aug 2013

Conference

Conference	2013 International Joint Conference on Neural Networks (IJCNN)
Country/Territory	United States
City	Texas
Period	4/08/13 → 9/08/13

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10.1109/IJCNN.2013.6707033

Cite this

@inproceedings{fbbbb71f184a4257bf59f6d9cdea68d3,

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.",

keywords = "Fuzzy Entropy, Support Vector Data Description",

author = "LE, {Trung Minh} and Dat TRAN and Tien Tran and Khanh Nyugen and Wanli MA",

year = "2013",

doi = "10.1109/IJCNN.2013.6707033",

language = "English",

isbn = "9781467361293",

volume = "1",

pages = "2339--2343",

editor = "Plaman Angelov and Daniel Levine",

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

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

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

}

LE, TM, TRAN, D, Tran, T, Nyugen, K & MA, W 2013, Fuzzy Entropy Semi-Supervised Support Vector Data Description. in P Angelov & D Levine (eds), The 2013 International Joint Conference on Neural Networks (IJCNN). vol. 1, IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 2339-2343, 2013 International Joint Conference on Neural Networks (IJCNN), Texas, United States, 4/08/13. https://doi.org/10.1109/IJCNN.2013.6707033

Fuzzy Entropy Semi-Supervised Support Vector Data Description. / LE, Trung Minh; TRAN, Dat; Tran, Tien et al.
The 2013 International Joint Conference on Neural Networks (IJCNN). ed. / Plaman Angelov; Daniel Levine. Vol. 1 USA: IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 2339-2343.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Fuzzy Entropy Semi-Supervised Support Vector Data Description

AU - LE, Trung Minh

AU - TRAN, Dat

AU - Tran, Tien

AU - Nyugen, Khanh

AU - MA, Wanli

PY - 2013

Y1 - 2013

N2 - 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.

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.

KW - Fuzzy Entropy

KW - Support Vector Data Description

U2 - 10.1109/IJCNN.2013.6707033

DO - 10.1109/IJCNN.2013.6707033

M3 - Conference contribution

SN - 9781467361293

VL - 1

SP - 2339

EP - 2343

BT - The 2013 International Joint Conference on Neural Networks (IJCNN)

A2 - Angelov, Plaman

A2 - Levine, Daniel

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - USA

T2 - 2013 International Joint Conference on Neural Networks (IJCNN)

Y2 - 4 August 2013 through 9 August 2013

ER -

Fuzzy Entropy Semi-Supervised Support Vector Data Description

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