[1]TENG Xuyang,DONG Hongbin,SUN Jing.Co-evolutionary algorithm for feature selection[J].CAAI Transactions on Intelligent Systems,2017,12(1):24-31.[doi:10.11992/tis.201611029]
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Co-evolutionary algorithm for feature selection
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
12
Number of periods:
2017 1
Page number:
24-31
Column:
学术论文—机器学习
Public date:
2017-02-25
- Title:
- Co-evolutionary algorithm for feature selection
- Keywords:
- feature selection; genetic algorithm (GA); particle swarm optimization (PSO); co-evolution; bit rate cross
- CLC:
- TP301
- DOI:
- 10.11992/tis.201611029
- Abstract:
- Feature selection is a key preprocessing technology of machine learning and data mining. The traditional greed type of feature selection methods only considers the best feature of the current round, thereby leading to the feature subset that is only locally optimal. Realizing an optimal or nearly optimal feature set is difficult. Evolutionary search means can effectively search for a feature space, but different evolutionary algorithms have their own limitations in search processes. The evolutionary advantages of genetic algorithms (GA) and particle swarm optimization (PSO) are absorbed in this study. The final feature subset is obtained by co-evolution, with the information entropy measure as an assessment function. A specific bit rate cross operator and an information exchange strategy applicable for a feature selection problem are proposed. The experimental results show that the co-evolutionary method (GA-PSO) is superior to the single evolutionary search method in the search ability of the feature subsets and classification learning. In conclusion, the ability of combined evaluation, which is provided by an evolutionary search, is better than that of the traditional greedy feature selection method.
- References:
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