A hybrid PSO-FSVM model and its application to imbalanced classification of mammograms

Hussein Samma; Chee Peng Lim; Umi Kalthum Ngah

doi:10.1007/978-3-642-36546-1_29

A hybrid PSO-FSVM model and its application to imbalanced classification of mammograms

Hussein Samma
, Chee Peng Lim^*
, Umi Kalthum Ngah

^*Corresponding author for this work

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

6 Scopus citations

Abstract

In this work, a hybrid model comprising Particle Swarm Optimization (PSO) and the Fuzzy Support Vector Machine (FSVM) for tackling imbalanced classification problems is proposed. A PSO algorithm, guided by the G-mean measure, is used to optimize the FSVM parameters in imbalanced classification problems. The hybrid PSO-FSVM model is evaluated using a mammogram mass classification problem. The experimental results are analyzed and compared with those from other methods. The outcomes positively demonstrate that the proposed PSO-FSVM model is able to achieve comparable, if not better, results for imbalanced data classification problems.

Original language	English
Title of host publication	Intelligent Information and Database Systems - 5th Asian Conference, ACIIDS 2013, Proceedings
Pages	275-284
Number of pages	10
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-36546-1_29
State	Published - 2013
Externally published	Yes
Event	5th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2013 - Kuala Lumpur, Malaysia Duration: 18 Mar 2013 → 20 Mar 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	7802 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2013
Country/Territory	Malaysia
City	Kuala Lumpur
Period	18/03/13 → 20/03/13

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-36546-1_29

Cite this

Samma, H., Lim, C. P., & Ngah, U. K. (2013). A hybrid PSO-FSVM model and its application to imbalanced classification of mammograms. In Intelligent Information and Database Systems - 5th Asian Conference, ACIIDS 2013, Proceedings (PART 1 ed., pp. 275-284). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7802 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-36546-1_29

Samma, Hussein ; Lim, Chee Peng ; Ngah, Umi Kalthum. / A hybrid PSO-FSVM model and its application to imbalanced classification of mammograms. Intelligent Information and Database Systems - 5th Asian Conference, ACIIDS 2013, Proceedings. PART 1. ed. 2013. pp. 275-284 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

@inproceedings{6f94fa454fb04917ba68547fd8f3d118,

title = "A hybrid PSO-FSVM model and its application to imbalanced classification of mammograms",

abstract = "In this work, a hybrid model comprising Particle Swarm Optimization (PSO) and the Fuzzy Support Vector Machine (FSVM) for tackling imbalanced classification problems is proposed. A PSO algorithm, guided by the G-mean measure, is used to optimize the FSVM parameters in imbalanced classification problems. The hybrid PSO-FSVM model is evaluated using a mammogram mass classification problem. The experimental results are analyzed and compared with those from other methods. The outcomes positively demonstrate that the proposed PSO-FSVM model is able to achieve comparable, if not better, results for imbalanced data classification problems.",

author = "Hussein Samma and Lim, \{Chee Peng\} and Ngah, \{Umi Kalthum\}",

year = "2013",

doi = "10.1007/978-3-642-36546-1\_29",

language = "English",

isbn = "9783642365454",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 1",

pages = "275--284",

booktitle = "Intelligent Information and Database Systems - 5th Asian Conference, ACIIDS 2013, Proceedings",

edition = "PART 1",

note = "5th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2013 ; Conference date: 18-03-2013 Through 20-03-2013",

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Samma, H, Lim, CP & Ngah, UK 2013, A hybrid PSO-FSVM model and its application to imbalanced classification of mammograms. in Intelligent Information and Database Systems - 5th Asian Conference, ACIIDS 2013, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7802 LNAI, pp. 275-284, 5th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2013, Kuala Lumpur, Malaysia, 18/03/13. https://doi.org/10.1007/978-3-642-36546-1_29

A hybrid PSO-FSVM model and its application to imbalanced classification of mammograms. / Samma, Hussein; Lim, Chee Peng; Ngah, Umi Kalthum.
Intelligent Information and Database Systems - 5th Asian Conference, ACIIDS 2013, Proceedings. PART 1. ed. 2013. p. 275-284 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7802 LNAI, No. PART 1).

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

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N2 - In this work, a hybrid model comprising Particle Swarm Optimization (PSO) and the Fuzzy Support Vector Machine (FSVM) for tackling imbalanced classification problems is proposed. A PSO algorithm, guided by the G-mean measure, is used to optimize the FSVM parameters in imbalanced classification problems. The hybrid PSO-FSVM model is evaluated using a mammogram mass classification problem. The experimental results are analyzed and compared with those from other methods. The outcomes positively demonstrate that the proposed PSO-FSVM model is able to achieve comparable, if not better, results for imbalanced data classification problems.

AB - In this work, a hybrid model comprising Particle Swarm Optimization (PSO) and the Fuzzy Support Vector Machine (FSVM) for tackling imbalanced classification problems is proposed. A PSO algorithm, guided by the G-mean measure, is used to optimize the FSVM parameters in imbalanced classification problems. The hybrid PSO-FSVM model is evaluated using a mammogram mass classification problem. The experimental results are analyzed and compared with those from other methods. The outcomes positively demonstrate that the proposed PSO-FSVM model is able to achieve comparable, if not better, results for imbalanced data classification problems.

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Samma H, Lim CP, Ngah UK. A hybrid PSO-FSVM model and its application to imbalanced classification of mammograms. In Intelligent Information and Database Systems - 5th Asian Conference, ACIIDS 2013, Proceedings. PART 1 ed. 2013. p. 275-284. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-36546-1_29

A hybrid PSO-FSVM model and its application to imbalanced classification of mammograms

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