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.

An New Fuzzy Support Vector Machine for Binary Classification

2012 ◽  
Vol 433-440 ◽  
pp. 2856-2861 ◽  
Author(s):  
Rui Zhang ◽  
Tong Bo Liu ◽  
Ming Wen Zheng
Keyword(s):  
Support Vector Machine ◽  
Binary Classification ◽  
Experimental Results ◽  
New Method ◽  
Support Vector ◽  
Fuzzy Support Vector Machine

In this paper, we proposed a new fuzzy support vector machine(called L2–FSVM here), which error part of object is L2–norm.Meanwhile we introduce a new method of generating fuzzy memberships so as to reduce to effects of outliers. The experimental results demonstrate that the L2-FSVM method provides improved ability to reduce to effects of outliers in comparison with traditional SVMs and FSVMs, and claim that L2–FSVM is the best way to solve the binary classification in the three methods stated above.

scholarly journals Recursive Feature Selection with Significant Variables of Support Vectors

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Chen-An Tsai ◽  
Chien-Hsun Huang ◽  
Ching-Wei Chang ◽  
Chun-Houh Chen
Keyword(s):  
Support Vector Machine ◽  
Gene Selection ◽  
Classification Performance ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Good Prediction ◽  
Simulation Experiments ◽  
Ranking Criteria ◽  
Good Prediction Accuracy ◽  
Microarray Datasets

The development of DNA microarray makes researchers screen thousands of genes simultaneously and it also helps determine high- and low-expression level genes in normal and disease tissues. Selecting relevant genes for cancer classification is an important issue. Most of the gene selection methods use univariate ranking criteria and arbitrarily choose a threshold to choose genes. However, the parameter setting may not be compatible to the selected classification algorithms. In this paper, we propose a new gene selection method (SVM-t) based on the use oft-statistics embedded in support vector machine. We compared the performance to two similar SVM-based methods: SVM recursive feature elimination (SVMRFE) and recursive support vector machine (RSVM). The three methods were compared based on extensive simulation experiments and analyses of two published microarray datasets. In the simulation experiments, we found that the proposed method is more robust in selecting informative genes than SVMRFE and RSVM and capable to attain good classification performance when the variations of informative and noninformative genes are different. In the analysis of two microarray datasets, the proposed method yields better performance in identifying fewer genes with good prediction accuracy, compared to SVMRFE and RSVM.

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.

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