Computer Science ›› 2013, Vol. 40 ›› Issue (7): 239-243.

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Feature Gene Selection Based on Improved Binary Particle Swarm Optimization Algorithm and its Application in Detection of Colon Cancer

CHAI Xin,SUN Jing-yao,GUO Lei and WU You-xi   

  • Online:2018-11-16 Published:2018-11-16

Abstract: In order to avoid local optimal solution of Binary Particle Swarm Optimization algorithm,an Improved Binary Particle Swarm Optimization (IBPSO) algorithm was presented.In this approach,the crossover and mutational strategies are introduced to increase the diversity of populations and avoid the premature-convergence of particles.Vaccine extraction,vaccination and immune selection are used to realize the vaccine mechanism to control the population degradation.In order to reduce the features of the tumor,Wilcoxon is used to remove the useless genes.IBPSO algorithm is used to optimize the subset of features and the parameters of Support Vector Machine (SVM).Finally,this method mentioned above is applied to detect the key genes of colon cancer dataset.The experimental results show that our approach can get higher classification accuracy with smaller size of feature subset than that of some other approaches and the selected genes are proven to be disease-causing.The experimental results also verify the correctness and effectiveness of our approach.

Key words: Feature selection,Particle swarm optimization algorithm,Support vector machine,Wilcoxon

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