Fuzzy multi-sphere support vector data description

Trung Le; Dat Tran; Wanli Ma

doi:10.1007/978-3-642-37456-2_48

Fuzzy multi-sphere support vector data description

Trung Le, Dat Tran, Wanli Ma

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

7 Citations (Scopus)

Abstract

Current well-known data description methods such as Support Vector Data Description and Small Sphere Large Margin are conducted with assumption that data samples of a class in feature space are drawn from a single distribution. Based on this assumption, a single hypersphere is constructed to provide a good data description for the data. However, real-world data samples may be drawn from some distinctive distributions and hence it does not guarantee that a single hypersphere can offer the best data description. In this paper, we introduce a Fuzzy Multi-sphere Support Vector Data Description approach to address this issue. We propose to use a set of hyperspheres to provide a better data description for a given data set. Calculations for determining optimal hyperspheres and experimental results for applying this proposed approach to classification problems are presented.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings
Publisher	Springer
Pages	570-581
Number of pages	12
Edition	PART 2
ISBN (Print)	9783642374555
DOIs	https://doi.org/10.1007/978-3-642-37456-2_48
Publication status	Published - 1 Dec 2013
Externally published	Yes
Event	Pacific-Asia Conference on Knowledge Discovery and Data Mining 2013 - Gold Coast, Australia Duration: 14 Apr 2013 → 17 Apr 2013 Conference number: 17th https://link.springer.com/book/10.1007/978-3-642-37453-1 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7819 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Pacific-Asia Conference on Knowledge Discovery and Data Mining 2013
Abbreviated title	PAKDD 2013
Country/Territory	Australia
City	Gold Coast
Period	14/04/13 → 17/04/13
Internet address	https://link.springer.com/book/10.1007/978-3-642-37453-1 (Proceedings)

Keywords

Fuzzy interference
Kernel methods
Multi-Sphere support vector data description
Support vector data description

Access to Document

10.1007/978-3-642-37456-2_48

Cite this

Le, T., Tran, D., & Ma, W. (2013). Fuzzy multi-sphere support vector data description. In Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings (PART 2 ed., pp. 570-581). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7819 LNAI, No. PART 2). Springer. https://doi.org/10.1007/978-3-642-37456-2_48

Le, Trung ; Tran, Dat ; Ma, Wanli. / Fuzzy multi-sphere support vector data description. Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings. PART 2. ed. Springer, 2013. pp. 570-581 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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abstract = "Current well-known data description methods such as Support Vector Data Description and Small Sphere Large Margin are conducted with assumption that data samples of a class in feature space are drawn from a single distribution. Based on this assumption, a single hypersphere is constructed to provide a good data description for the data. However, real-world data samples may be drawn from some distinctive distributions and hence it does not guarantee that a single hypersphere can offer the best data description. In this paper, we introduce a Fuzzy Multi-sphere Support Vector Data Description approach to address this issue. We propose to use a set of hyperspheres to provide a better data description for a given data set. Calculations for determining optimal hyperspheres and experimental results for applying this proposed approach to classification problems are presented.",

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Le, T, Tran, D & Ma, W 2013, Fuzzy multi-sphere support vector data description. in Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7819 LNAI, Springer, pp. 570-581, Pacific-Asia Conference on Knowledge Discovery and Data Mining 2013, Gold Coast, Queensland, Australia, 14/04/13. https://doi.org/10.1007/978-3-642-37456-2_48

Fuzzy multi-sphere support vector data description. / Le, Trung; Tran, Dat; Ma, Wanli.

Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings. PART 2. ed. Springer, 2013. p. 570-581 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7819 LNAI, No. PART 2).

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

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AU - Tran, Dat

AU - Ma, Wanli

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AB - Current well-known data description methods such as Support Vector Data Description and Small Sphere Large Margin are conducted with assumption that data samples of a class in feature space are drawn from a single distribution. Based on this assumption, a single hypersphere is constructed to provide a good data description for the data. However, real-world data samples may be drawn from some distinctive distributions and hence it does not guarantee that a single hypersphere can offer the best data description. In this paper, we introduce a Fuzzy Multi-sphere Support Vector Data Description approach to address this issue. We propose to use a set of hyperspheres to provide a better data description for a given data set. Calculations for determining optimal hyperspheres and experimental results for applying this proposed approach to classification problems are presented.

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Le T, Tran D, Ma W. Fuzzy multi-sphere support vector data description. In Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings. PART 2 ed. Springer. 2013. p. 570-581. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-37456-2_48

Fuzzy multi-sphere support vector data description

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Conference

Keywords

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