Computer Science ›› 2014, Vol. 41 ›› Issue (10): 291-294.doi: 10.11896/j.issn.1002-137X.2014.10.061

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Hybrid Gene Selection Algorithm Based on Optimized Neighborhood Rough Set

CHEN Tao,HONG Zeng-lin and DENG Fang-an   

  • Online:2018-11-14 Published:2018-11-14

Abstract: DNA microarray technique can detect tens of thousands of gene activity in cells,which has been widely used in clinical diagnosis.However,microarray data has high dimension,small sample,a lot of noise and redundant genes.In order to further improve the classification performance,this paper proposed a hybrid gene selection algorithm.Firstly,using ReliefF algorithm to eliminate a lot of irrelevant genes,the feature genes candidate set was obtained.Then the optimized neighborhood rough set model based on differential evolution algorithm was used to select feature genes.At last the validity of the algorithm was verified using support vector machine as classifier.The simulation results show that the algorithm can obtain higher classification accuracy with less feature gene,and it not only enhances the generalization performance of the algorithm,but also improves the time efficiency.

Key words: Feature gene selection,ReliefF algorithm,Neighborhood rough set model,Differential evolution algorithm

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