Fuzzy semi-supervised large margin One-Class Support Vector Machine

Trung Le; Van Nguyen; Thien Pham; Mi Dinh; Thai Hoang Le

doi:10.1007/978-3-319-14633-1_5

Fuzzy semi-supervised large margin One-Class Support Vector Machine

Trung Le, Van Nguyen, Thien Pham, Mi Dinh, Thai Hoang Le

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

6 Citations (Scopus)

Abstract

One-class Support Vector Machine (OCSVM) is one of state-of-the-art kernel-based methods for one-class classification problem. OCSVM produces the good performance for imbalanced dataset. Nonetheless, it cannot make use of negative data samples and also cannot utilize unlabeled data to boost the classifier. In this paper, we first extend the model of OCSVM to make use of the information carried by negative data samples for classification and then propose how to integrate the semi-supervised paradigm to the extended OCSVM for utilizing the unlabeled data to increase the classifier’s generalization ability. Finally, we show how to apply the fuzzy theory to the proposed semi-supervised one-class classification method for efficiently handling noises and outliers.

Original language	English
Title of host publication	Some Current Advanced Researches on Information and Computer Science in Vietnam
Subtitle of host publication	Post-proceedings of The First NAFOSTED Conference on Information and Computer Science
Editors	Quang A Dang, Xuan Hoai Nguyen, Hoai Bac Le, Viet Ha Nguyen, Vo Nguyen Quoc Bao
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	65-78
Number of pages	14
ISBN (Electronic)	9783319146331
ISBN (Print)	9783319146324
DOIs	https://doi.org/10.1007/978-3-319-14633-1_5
Publication status	Published - 2015
Externally published	Yes
Event	National Foundation for Science and Technology Development Conference on Information and Computer Science 2015 - Ho Chi Minh, Vietnam Duration: 16 Sept 2015 → 18 Sept 2015 Conference number: 1st https://edas.info/web/nics14/index.html https://web.archive.org/web/20150807190107/http://nafosted-nics.org/

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	341
ISSN (Print)	2194-5357

Conference

Conference	National Foundation for Science and Technology Development Conference on Information and Computer Science 2015
Abbreviated title	NICS 2015
Country/Territory	Vietnam
City	Ho Chi Minh
Period	16/09/15 → 18/09/15
Internet address	https://edas.info/web/nics14/index.html https://web.archive.org/web/20150807190107/http://nafosted-nics.org/

Keywords

Fuzzy membership
Novelty detection
One-class classification
S3VM
Semi-supervised learning
Support vector machine

Access to Document

10.1007/978-3-319-14633-1_5

Cite this

Le, T., Nguyen, V., Pham, T., Dinh, M., & Le, T. H. (2015). Fuzzy semi-supervised large margin One-Class Support Vector Machine. In Q. A. Dang, X. H. Nguyen, H. B. Le, V. Ha Nguyen, & V. N. Q. Bao (Eds.), Some Current Advanced Researches on Information and Computer Science in Vietnam: Post-proceedings of The First NAFOSTED Conference on Information and Computer Science (pp. 65-78). (Advances in Intelligent Systems and Computing; Vol. 341). Springer. https://doi.org/10.1007/978-3-319-14633-1_5

Le, Trung ; Nguyen, Van ; Pham, Thien et al. / Fuzzy semi-supervised large margin One-Class Support Vector Machine. Some Current Advanced Researches on Information and Computer Science in Vietnam: Post-proceedings of The First NAFOSTED Conference on Information and Computer Science. editor / Quang A Dang ; Xuan Hoai Nguyen ; Hoai Bac Le ; Viet Ha Nguyen ; Vo Nguyen Quoc Bao. Cham Switzerland : Springer, 2015. pp. 65-78 (Advances in Intelligent Systems and Computing).

@inproceedings{f0c99579e59d443194222b357f8ea814,

title = "Fuzzy semi-supervised large margin One-Class Support Vector Machine",

abstract = "One-class Support Vector Machine (OCSVM) is one of state-of-the-art kernel-based methods for one-class classification problem. OCSVM produces the good performance for imbalanced dataset. Nonetheless, it cannot make use of negative data samples and also cannot utilize unlabeled data to boost the classifier. In this paper, we first extend the model of OCSVM to make use of the information carried by negative data samples for classification and then propose how to integrate the semi-supervised paradigm to the extended OCSVM for utilizing the unlabeled data to increase the classifier{\textquoteright}s generalization ability. Finally, we show how to apply the fuzzy theory to the proposed semi-supervised one-class classification method for efficiently handling noises and outliers.",

keywords = "Fuzzy membership, Novelty detection, One-class classification, S3VM, Semi-supervised learning, Support vector machine",

author = "Trung Le and Van Nguyen and Thien Pham and Mi Dinh and Le, {Thai Hoang}",

year = "2015",

doi = "10.1007/978-3-319-14633-1_5",

language = "English",

isbn = "9783319146324",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

pages = "65--78",

editor = "Dang, {Quang A} and Nguyen, {Xuan Hoai} and Le, {Hoai Bac} and {Ha Nguyen}, Viet and Bao, {Vo Nguyen Quoc}",

booktitle = "Some Current Advanced Researches on Information and Computer Science in Vietnam",

note = "National Foundation for Science and Technology Development Conference on Information and Computer Science 2015, NICS 2015 ; Conference date: 16-09-2015 Through 18-09-2015",

url = "https://edas.info/web/nics14/index.html, https://web.archive.org/web/20150807190107/http://nafosted-nics.org/",

}

Le, T , Nguyen, V, Pham, T, Dinh, M & Le, TH 2015, Fuzzy semi-supervised large margin One-Class Support Vector Machine. in QA Dang, XH Nguyen, HB Le, V Ha Nguyen & VNQ Bao (eds), Some Current Advanced Researches on Information and Computer Science in Vietnam: Post-proceedings of The First NAFOSTED Conference on Information and Computer Science. Advances in Intelligent Systems and Computing, vol. 341, Springer, Cham Switzerland, pp. 65-78, National Foundation for Science and Technology Development Conference on Information and Computer Science 2015, Ho Chi Minh, Vietnam, 16/09/15. https://doi.org/10.1007/978-3-319-14633-1_5

Fuzzy semi-supervised large margin One-Class Support Vector Machine. / Le, Trung ; Nguyen, Van; Pham, Thien et al.
Some Current Advanced Researches on Information and Computer Science in Vietnam: Post-proceedings of The First NAFOSTED Conference on Information and Computer Science. ed. / Quang A Dang; Xuan Hoai Nguyen; Hoai Bac Le; Viet Ha Nguyen; Vo Nguyen Quoc Bao. Cham Switzerland: Springer, 2015. p. 65-78 (Advances in Intelligent Systems and Computing; Vol. 341).

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

TY - GEN

T1 - Fuzzy semi-supervised large margin One-Class Support Vector Machine

AU - Le, Trung

AU - Nguyen, Van

AU - Pham, Thien

AU - Dinh, Mi

AU - Le, Thai Hoang

N1 - Conference code: 1st

PY - 2015

Y1 - 2015

N2 - One-class Support Vector Machine (OCSVM) is one of state-of-the-art kernel-based methods for one-class classification problem. OCSVM produces the good performance for imbalanced dataset. Nonetheless, it cannot make use of negative data samples and also cannot utilize unlabeled data to boost the classifier. In this paper, we first extend the model of OCSVM to make use of the information carried by negative data samples for classification and then propose how to integrate the semi-supervised paradigm to the extended OCSVM for utilizing the unlabeled data to increase the classifier’s generalization ability. Finally, we show how to apply the fuzzy theory to the proposed semi-supervised one-class classification method for efficiently handling noises and outliers.

AB - One-class Support Vector Machine (OCSVM) is one of state-of-the-art kernel-based methods for one-class classification problem. OCSVM produces the good performance for imbalanced dataset. Nonetheless, it cannot make use of negative data samples and also cannot utilize unlabeled data to boost the classifier. In this paper, we first extend the model of OCSVM to make use of the information carried by negative data samples for classification and then propose how to integrate the semi-supervised paradigm to the extended OCSVM for utilizing the unlabeled data to increase the classifier’s generalization ability. Finally, we show how to apply the fuzzy theory to the proposed semi-supervised one-class classification method for efficiently handling noises and outliers.

KW - Fuzzy membership

KW - Novelty detection

KW - One-class classification

KW - S3VM

KW - Semi-supervised learning

KW - Support vector machine

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U2 - 10.1007/978-3-319-14633-1_5

DO - 10.1007/978-3-319-14633-1_5

M3 - Conference Paper

AN - SCOPUS:84943253787

SN - 9783319146324

T3 - Advances in Intelligent Systems and Computing

SP - 65

EP - 78

BT - Some Current Advanced Researches on Information and Computer Science in Vietnam

A2 - Dang, Quang A

A2 - Nguyen, Xuan Hoai

A2 - Le, Hoai Bac

A2 - Ha Nguyen, Viet

A2 - Bao, Vo Nguyen Quoc

PB - Springer

CY - Cham Switzerland

T2 - National Foundation for Science and Technology Development Conference on Information and Computer Science 2015

Y2 - 16 September 2015 through 18 September 2015

ER -

Le T , Nguyen V, Pham T, Dinh M, Le TH. Fuzzy semi-supervised large margin One-Class Support Vector Machine. In Dang QA, Nguyen XH, Le HB, Ha Nguyen V, Bao VNQ, editors, Some Current Advanced Researches on Information and Computer Science in Vietnam: Post-proceedings of The First NAFOSTED Conference on Information and Computer Science. Cham Switzerland: Springer. 2015. p. 65-78. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-14633-1_5

Fuzzy semi-supervised large margin One-Class Support Vector Machine

Abstract

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Keywords

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