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 contribution

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 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
Pages2339-2343
Number of pages5
Volume1
ISBN (Print)9781467361293
DOIs
Publication statusPublished - 2013
Event2013 International Joint Conference on Neural Networks (IJCNN) - Dallas, Texas, United States
Duration: 4 Aug 20139 Aug 2013

Conference

Conference2013 International Joint Conference on Neural Networks (IJCNN)
CountryUnited States
CityTexas
Period4/08/139/08/13

Fingerprint

Data description
Entropy
Labeling
Supervised learning

Cite this

LE, T. M., 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, pp. 2339-2343). USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2013.6707033
LE, Trung Minh ; TRAN, Dat ; Tran, Tien ; Nyugen, Khanh ; MA, Wanli. / Fuzzy Entropy Semi-Supervised Support Vector Data Description. The 2013 International Joint Conference on Neural Networks (IJCNN). editor / Plaman Angelov ; Daniel Levine. Vol. 1 USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 2339-2343
@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",

}

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; Nyugen, Khanh; MA, Wanli.

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 BookConference contribution

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

ER -

LE TM, TRAN D, Tran T, Nyugen K, MA W. Fuzzy Entropy Semi-Supervised Support Vector Data Description. In Angelov P, Levine D, editors, The 2013 International Joint Conference on Neural Networks (IJCNN). Vol. 1. USA: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 2339-2343 https://doi.org/10.1109/IJCNN.2013.6707033