Computer Science ›› 2017, Vol. 44 ›› Issue (3): 237-241.doi: 10.11896/j.issn.1002-137X.2017.03.049

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Feature Processing Approach Based on MA-LSSVM in Safety Data

MA Yuan-yuan, SHI Yong-yi, ZHANG Hong, LIN Qi and LI Qian-mu   

  • Online:2018-11-13 Published:2018-11-13

Abstract: With all kinds of biological intelligent evolutionary algorithms become increasingly mature,feature selection methods based on the evolutionary technology and its hybrid algorithm are emerging.According to the feature selection problem of the high dimensional small sample safety data,this paper combined memetic algorithm (MA) and least squares support vector machines (LS-SVM) to design a kind of wrapper feature selection method (MA-LSSVM).The proposed method utilizes the specialty of least squares support vector machine being easy to search optimal solution to construct classifier,then regards classification accuracy as the main component of memetic algorithm fitness function in the optimization process.The experimental results demonstrate that MA-LSSVM can be more efficient and stable to obtain features larger contribution to the classification precision,and can reduce the data dimension and improve the classification efficiency.

Key words: Feature selection,Memetic algorithm,Least squares support vector machine,Stability

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