吉林大学学报(工学版) ›› 2011, Vol. 41 ›› Issue (6): 1690-1693.

• paper • Previous Articles     Next Articles

Feature selection based on adaptive multi-population genetic algorithm

LIU Yuan-ning1,2, WANG Gang1,2, ZHU Xiao-dong1,2, ZHAO Zheng-dong3, CHEN Hui-ling1,2, XING Chong1,2   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012,China|2.Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China|3.Network Center, Changchun University of Science and Technology, Changchun 130022, China
  • Received:2010-07-19 Online:2011-11-01 Published:2011-11-01

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Abstract:

An Adaptive Multi-population Genetic Algorithm (AMGA) was proposed, which was applied to feature selection and to find the optimal feature subset from high dimensional feature sets. AMGA consists of a Multi-population Planning (MPP) module and a Dynamic Selection Algorithm (DSA), both being self-designed. The proposed method extends the search space and adjusts the running states of multi-population, thereby controlling the premature convergence and increasing the local search capacity for the premature convergence. Segment from UCI and StatLog data sets were used to evaluate the proposed method. Results show that, comparing with standard genetic algorithm, the proposed method obtained an ideal result with less selected features and higher classification accuracy. The proposed method can be widely applied in the field of feature selection.

Key words: artificial intelligence, genetic algorithm, support vector machines, feature selection

CLC Number: 

  • TP18
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