Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 93-101.doi: 10.11896/jsjkx.210100067

• Intelligent Computing • Previous Articles     Next Articles

Hybrid Artificial Chemical Reaction Optimization with Wolf Colony Algorithm for Feature Selection

ZHANG Ya-chuan1, LI Hao1, SONG Chen-ming2, BU Rong-jing1, WANG Hai-ning1, KANG Yan1   

  1. 1 School of Software,Yunnan University,Kunming 650500,China
    2 School of Software,Xi'an Jiaotong University,Xi'an 710000,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:ZHANG Ya-chuan,born in 1996,postgraduate.Her main research interests include swarm intelligence optimization and data mining.
    KANG Yan,born in 1972,Ph.D,associate professor. Her main research interests include transfer learning,deep learning and integrated learning.
  • Supported by:
    National Natural Science Foundation of China(61762092),Yunnan Key Laboratory of Software Engineering Open Fund Project(2017SE204),Yunnan Major Science and Technology Project(202002AB080001) and Material Genetic Engineering-Metcloud Based Integrated Computing Function Module Computing Software Development(2019CLJY06).

Abstract: Wrapper feature selection is a data preprocessing method for reducing original dataset dimensionality by screening the most informative features to maximize the classification accuracy synchronously.In order to improve the wrapper feature selection ability,this paper proposes a hybrid artificial chemical reaction wolf colony optimization algorithm for selecting feature-ACR-WCA.First,ACR-WCA algorithm adopts natural strategy,imitates the search strategy of wolves,so can quickly approach the solution space.Secondly,in order to deal with data features effectively,the S-shaped transfer function is used to deal with binary features in the initialization stage.Then the fitness function of the algorithm is given by combining classification accuracy and the number of features.Meanwhile,the method uses K-Nearest Neighbor (KNN) classifier for training and tested data by K-fold cross-validation to overcome the over fitting problem.The experiments are trained based on 21 famous and different dimensionality dataset,and compared with four traditional methods and three nearly methods.Experimental results show that the algorithm is efficient and reliable.It can select the most features for classifications tasks with high accuracy.

Key words: Artificial chemical reaction optimization, Classification, Feature selection, Optimization, Wolf colony algorithm

CLC Number: 

  • TP391
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