An Improved Feature Selection Algorithm Based on Ant Colony Optimization

• Published in: IEEE access Vol. 6; pp. 69203 - 69209
• Main Authors: Peng, Huijun, Ying, Chun, Tan, Shuhua, Hu, Bing, Sun, Zhixin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Ant colony optimization; Classification; Classifiers; Data mining; Feature extraction; Feature selection; Intrusion detection; Optimization; Particle swarm optimization; Search problems
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2879583

The diversity and complexity of network data bring great challenges to data classification technology. Feature selection has always been an important and difficult problem in classification technology. To improve the classification performance of the classifier, an improved feature selection algorithm, FACO, is proposed by combining the ant colony optimization algorithm and feature selection. A fitness function is designed, and the pheromone updating rule is optimized to effectively eliminate redundant features and prevent feature selection from falling into a local optimum. The experimental results show that the classification accuracy of the classifier can be significantly improved by selecting the data features using the FACO algorithm, which is of practical significance.