Gene Selection for Cancer Classification using Wilcoxon Rank Sum Test and Support Vector Machine

2006 ◽  
Author(s):  
Chen Liao ◽  
Shutao Li ◽  
Zhiyuan Luo
Keyword(s):  
Support Vector Machine ◽  
Gene Selection ◽  
Cancer Classification ◽  
Support Vector ◽  
Wilcoxon Rank Sum Test ◽  
Selection For

scholarly journals Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Maolong Xi ◽  
Jun Sun ◽  
Li Liu ◽  
Fangyun Fan ◽  
Xiaojun Wu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Gene Selection ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Cancer Classification ◽  
Support Vector ◽  
Swarm Optimization ◽  
Binary Pso ◽  
Leave One Out

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.

scholarly journals Multiclass Cancer Classification by Using Fuzzy Support Vector Machine and Binary Decision Tree With Gene Selection

2005 ◽  
Vol 2005 (2) ◽  
pp. 160-171 ◽  
Author(s):  
Yong Mao ◽  
Xiaobo Zhou ◽  
Daoying Pi ◽  
Youxian Sun ◽  
Stephen T. C. Wong
Keyword(s):  
Support Vector Machine ◽  
Gene Selection ◽  
Binary Classification ◽  
Classification Tree ◽  
Cancer Classification ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
F Test ◽  
Leukemia Data

We investigate the problems of multiclass cancer classification with gene selection from gene expression data. Two different constructed multiclass classifiers with gene selection are proposed, which are fuzzy support vector machine (FSVM) with gene selection and binary classification tree based on SVM with gene selection. Using F test and recursive feature elimination based on SVM as gene selection methods, binary classification tree based on SVM with F test, binary classification tree based on SVM with recursive feature elimination based on SVM, and FSVM with recursive feature elimination based on SVM are tested in our experiments. To accelerate computation, preselecting the strongest genes is also used. The proposed techniques are applied to analyze breast cancer data, small round blue-cell tumors, and acute leukemia data. Compared to existing multiclass cancer classifiers and binary classification tree based on SVM with F test or binary classification tree based on SVM with recursive feature elimination based on SVM mentioned in this paper, FSVM based on recursive feature elimination based on SVM can find most important genes that affect certain types of cancer with high recognition accuracy.

Sign in / Sign up

Export Citation Format

Share Document