吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (01): 137-141.doi: 10.13229/j.cnki.jdxbgxb201401024

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Feature selection algorithm based on random forest

YAO Deng-ju1,2, YANG Jing1, ZHAN Xiao-juan3   

  1. 1. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China;
    2. School of Software, Harbin University of Science and Technology, Harbin 150040, China;
    3. College of Computer Science and Technology, Heilongjiang Institute of Technology, Harbin 150050, China
  • Received:2012-08-21 Online:2014-01-01 Published:2014-01-01

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Abstract:

A feature selection algorithm based on random forest (RFFS) is proposed. This algorithm adopts random forest algorithm as the basic tool, the classification accuracy as the criterion function. The sequential backward selection and generalized sequential backward selection methods are employed for feature selection. The experimental results on UCI datasets show that the RFFS algorithm has better performance in classification accuracy and feature selection subset than the other methods in literatures.

Key words: artificial intelligence, random forest, feature selection, wrapper

CLC Number: 

  • TP18

[1] 蒋胜利. 高维数据的特征选择与特征提取研究[D]. 西安:西安电子科技大学计算机学院, 2011. Jiang Sheng-li. Research on feature selection and feature extraction for high-dimensional data[D]. Xi'an: School of Computer Science and Engineering, Xidian University, 2011.

[2] Davies S, Russl S. NP-completeness of searches for smallest possible feature sets[C]//Proceedings of the AAAI Fall Symposiums on Relevance, Menlo Park, 1994: 37-39.

[3] Breiman L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.

[4] Strobl Carolin, Boulesteix Anne-Laure, Kneib Thomas, et al. Conditional variable importance for random forests[J]. BMC Bioinformatics, 2008, 9(1): 1-11.

[5] Reif David M, Motsinger Alison A, McKinney Brett A, et al. Feature selection using a random forests classifier for the integrated analysis of multiple data types[C]//IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, 2006: 171-178.

[6] Mohammed Khalilia, Sounak Chakraborty, Mihail Popescu. Predicting disease risks from highly imbalanced data using random forest[J]. BMC Medical Informatics and Decision Making, 2011, 11(7): 51-58.

[7] Verikas A, Gelzinis A, Bacauskiene M. Mining data with random forests: a survey and results of new tests[J]. Pattern Recognition, 2011, 44(2): 330-349.

[8] Inza I, Larranaga P, Blanco R. Filter versus wrapper gene selection approaches in DNA microarray domains[J]. Artificial Intelligence in Medicine, 2004, 31(2): 91-103.

[9] 蒋盛益, 郑琪, 张倩生. 基于聚类的特征选择方法[J]. 电子学报, 2008, 36(12):157-160. Jiang Sheng-yi, Zheng Qi, Zhang Qian-sheng. Clustering-based feature selection[J]. Acta Electronica Sinica, 2008, 36(12):157-160.

[10] 刘元宁, 王刚, 朱晓冬, 等. 基于自适应多种群遗传算法的特征选择[J]. 吉林大学学报:工学版, 2011, 41(6): 1690-1693. Liu Yuan-ning, Wang Gang, Zhu Xiao-dong, et al.Feature selection based on adaptive multi-population genetic algorithm[J].Journal of Jilin University(Engineering and Technology Edition), 2011, 41(6):1690-1693.
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