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A New Hybrid Ant Colony Optimization Based on Brain Storm Optimization for Feature Selection

Haomo LIANG, Zhixue WANG, Yi LIU

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Summary :

Machine learning algorithms are becoming more and more popular in current era. Data preprocessing especially feature selection is helpful for improving the performance of those algorithms. A new powerful feature selection algorithm is proposed. It combines the advantages of ant colony optimization and brain storm optimization which simulates the behavior of human beings. Six classical datasets and five state-of-art algorithms are used to make a comparison with our algorithm on binary classification problems. The results on accuracy, percent rate, recall rate, and F1 measures show that the developed algorithm is more excellent. Besides, it is no more complex than the compared approaches.

Publication
IEICE TRANSACTIONS on Information Vol.E102-D No.7 pp.1396-1399
Publication Date
2019/07/01
Publicized
2019/04/12
Online ISSN
1745-1361
DOI
10.1587/transinf.2019EDL8001
Type of Manuscript
LETTER
Category
Fundamentals of Information Systems

Authors

Haomo LIANG
  Army Engineering University of PLA
Zhixue WANG
  Army Engineering University of PLA
Yi LIU
  National Innovation Institute of Defense Technology

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