Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine
Joint Authors
Xi, Maolong
Sun, Jun
Liu, Li
Fan, Fangyun
Wu, Xiaojun
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-24
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes.
We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification.
First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems.
Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV).
Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma.
The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.
American Psychological Association (APA)
Xi, Maolong& Sun, Jun& Liu, Li& Fan, Fangyun& Wu, Xiaojun. 2016. Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1100114
Modern Language Association (MLA)
Xi, Maolong…[et al.]. Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1100114
American Medical Association (AMA)
Xi, Maolong& Sun, Jun& Liu, Li& Fan, Fangyun& Wu, Xiaojun. Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1100114
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100114