A constraint and rule in an enhancement of binary particle swarm optimization to select informative genes for cancer classification

Mohd Saberi Mohamad; Sigeru Omatu; Safaai Deris; Michifumi Yoshioka

doi:10.1007/978-3-642-40319-4_15

A constraint and rule in an enhancement of binary particle swarm optimization to select informative genes for cancer classification

Mohd Saberi Mohamad
, Sigeru Omatu
, Safaai Deris
, Michifumi Yoshioka

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

4 Citations (Scopus)

Abstract

Gene expression data have been analyzing by many researchers by using a range of computational intelligence methods. From the gene expression data, selecting a small subset of informative genes can do cancer classification. Nevertheless, many of the computational methods face difficulties in selecting small subset since the small number of samples needs to be compared to the huge number of genes (high-dimension), irrelevant genes and noisy genes. Hence, to choose the small subset of informative genes that is significant for the cancer classification, an enhanced binary particle swarm optimization is proposed. Here, the constraint of the elements of particle velocity vectors is introduced and a rule for updating particle's position is proposed. Experiments were performed on five different gene expression data. As a result, in terms of classification accuracy and the number of selected genes, the performance of the introduced method is superior compared to the conventional version of binary particle swarm optimization (BPSO). The other significant finding is lower running times compared to BPSO for this proposed method.

Original language	English
Title of host publication	Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops
Subtitle of host publication	DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers
Pages	168-178
Number of pages	11
DOIs	https://doi.org/10.1007/978-3-642-40319-4_15
Publication status	Published - 2013
Externally published	Yes
Event	17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013 - Gold Coast, QLD, Australia Duration: Apr 14 2013 → Apr 17 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7867 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
Country/Territory	Australia
City	Gold Coast, QLD
Period	4/14/13 → 4/17/13

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Binary particle swarm optimization
Gene expression data
Gene selection
Optimization

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-40319-4_15

Cite this

Mohamad, M. S., Omatu, S., Deris, S., & Yoshioka, M. (2013). A constraint and rule in an enhancement of binary particle swarm optimization to select informative genes for cancer classification. In Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers (pp. 168-178). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7867 LNAI). https://doi.org/10.1007/978-3-642-40319-4_15

A constraint and rule in an enhancement of binary particle swarm optimization to select informative genes for cancer classification. / Mohamad, Mohd Saberi; Omatu, Sigeru; Deris, Safaai et al.
Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers. 2013. p. 168-178 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7867 LNAI).

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

Mohamad, MS, Omatu, S, Deris, S & Yoshioka, M 2013, A constraint and rule in an enhancement of binary particle swarm optimization to select informative genes for cancer classification. in Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7867 LNAI, pp. 168-178, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013, Gold Coast, QLD, Australia, 4/14/13. https://doi.org/10.1007/978-3-642-40319-4_15

Mohamad MS, Omatu S, Deris S, Yoshioka M. A constraint and rule in an enhancement of binary particle swarm optimization to select informative genes for cancer classification. In Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers. 2013. p. 168-178. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-40319-4_15

Mohamad, Mohd Saberi ; Omatu, Sigeru ; Deris, Safaai et al. / A constraint and rule in an enhancement of binary particle swarm optimization to select informative genes for cancer classification. Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers. 2013. pp. 168-178 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Gene expression data have been analyzing by many researchers by using a range of computational intelligence methods. From the gene expression data, selecting a small subset of informative genes can do cancer classification. Nevertheless, many of the computational methods face difficulties in selecting small subset since the small number of samples needs to be compared to the huge number of genes (high-dimension), irrelevant genes and noisy genes. Hence, to choose the small subset of informative genes that is significant for the cancer classification, an enhanced binary particle swarm optimization is proposed. Here, the constraint of the elements of particle velocity vectors is introduced and a rule for updating particle's position is proposed. Experiments were performed on five different gene expression data. As a result, in terms of classification accuracy and the number of selected genes, the performance of the introduced method is superior compared to the conventional version of binary particle swarm optimization (BPSO). The other significant finding is lower running times compared to BPSO for this proposed method.",

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AU - Deris, Safaai

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PY - 2013

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AB - Gene expression data have been analyzing by many researchers by using a range of computational intelligence methods. From the gene expression data, selecting a small subset of informative genes can do cancer classification. Nevertheless, many of the computational methods face difficulties in selecting small subset since the small number of samples needs to be compared to the huge number of genes (high-dimension), irrelevant genes and noisy genes. Hence, to choose the small subset of informative genes that is significant for the cancer classification, an enhanced binary particle swarm optimization is proposed. Here, the constraint of the elements of particle velocity vectors is introduced and a rule for updating particle's position is proposed. Experiments were performed on five different gene expression data. As a result, in terms of classification accuracy and the number of selected genes, the performance of the introduced method is superior compared to the conventional version of binary particle swarm optimization (BPSO). The other significant finding is lower running times compared to BPSO for this proposed method.

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KW - Gene selection

KW - Optimization

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A constraint and rule in an enhancement of binary particle swarm optimization to select informative genes for cancer classification

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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