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A Novel Weighted Support Vector Machine Based on Particle Swarm Optimization for Gene Selection and Tumor Classification
Joint Authors
Abdi, Mohammad Javad
Hosseini, Seyed Mohammad
Rezghi, Mansoor
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-07-26
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
We develop a detection model based on support vector machines (SVMs) and particle swarm optimization (PSO) for gene selection and tumor classification problems.
The proposed model consists of two stages: first, the well-known minimum redundancy-maximum relevance (mRMR) method is applied to preselect genes that have the highest relevance with the target class and are maximally dissimilar to each other.
Then, PSO is proposed to form a novel weighted SVM (WSVM) to classify samples.
In this WSVM, PSO not only discards redundant genes, but also especially takes into account the degree of importance of each gene and assigns diverse weights to the different genes.
We also use PSO to find appropriate kernel parameters since the choice of gene weights influences the optimal kernel parameters and vice versa.
Experimental results show that the proposed mRMR-PSO-WSVM model achieves highest classification accuracy on two popular leukemia and colon gene expression datasets obtained from DNA microarrays.
Therefore, we can conclude that our proposed method is very promising compared to the previously reported results.
American Psychological Association (APA)
Abdi, Mohammad Javad& Hosseini, Seyed Mohammad& Rezghi, Mansoor. 2012. A Novel Weighted Support Vector Machine Based on Particle Swarm Optimization for Gene Selection and Tumor Classification. Computational and Mathematical Methods in Medicine،Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-463288
Modern Language Association (MLA)
Abdi, Mohammad Javad…[et al.]. A Novel Weighted Support Vector Machine Based on Particle Swarm Optimization for Gene Selection and Tumor Classification. Computational and Mathematical Methods in Medicine No. 2012 (2012), pp.1-7.
https://search.emarefa.net/detail/BIM-463288
American Medical Association (AMA)
Abdi, Mohammad Javad& Hosseini, Seyed Mohammad& Rezghi, Mansoor. A Novel Weighted Support Vector Machine Based on Particle Swarm Optimization for Gene Selection and Tumor Classification. Computational and Mathematical Methods in Medicine. 2012. Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-463288
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-463288