Work

An Improved Feature Selection Algorithm Based on Ant Colony Optimization

Year: 2018

Type: article

Abstract: 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.

Source: IEEE Access

DOI: 10.1109/access.2018.2879583

Authors: Huijun Peng, Chun Ying, Shuhua Tan, Bing Hu, Zhixin Sun

Institution: Nanjing University of Posts and Telecommunications

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funders: National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province