An Improved Feature Selection Algorithm Based on Ant Colony Optimization
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 algorit...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8532385/ |
| _version_ | 1818428966088736768 |
|---|---|
| author | Huijun Peng Chun Ying Shuhua Tan Bing Hu Zhixin Sun |
| author_facet | Huijun Peng Chun Ying Shuhua Tan Bing Hu Zhixin Sun |
| author_sort | Huijun Peng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T15:10:01Z |
| format | Article |
| id | doaj.art-8dc2b5dce54e47a8b318846045df3f87 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T15:10:01Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8dc2b5dce54e47a8b318846045df3f872022-12-21T22:56:36ZengIEEEIEEE Access2169-35362018-01-016692036920910.1109/ACCESS.2018.28795838532385An Improved Feature Selection Algorithm Based on Ant Colony OptimizationHuijun Peng0https://orcid.org/0000-0003-0334-3263Chun Ying1Shuhua Tan2https://orcid.org/0000-0003-1558-2704Bing Hu3https://orcid.org/0000-0002-4037-4352Zhixin Sun4Key Laboratory of Broadband Wireless Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Nanjing, ChinaNational Engineering Laboratory for Logistics Information Technology, Yuantong Express Company Ltd., Shanghai, ChinaNational Engineering Laboratory for Logistics Information Technology, Yuantong Express Company Ltd., Shanghai, ChinaKey Laboratory of Broadband Wireless Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Nanjing, ChinaKey Laboratory of Broadband Wireless Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Nanjing, ChinaThe 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.https://ieeexplore.ieee.org/document/8532385/Feature extractionant colony optimizationintrusion detection |
| spellingShingle | Huijun Peng Chun Ying Shuhua Tan Bing Hu Zhixin Sun An Improved Feature Selection Algorithm Based on Ant Colony Optimization IEEE Access Feature extraction ant colony optimization intrusion detection |
| title | An Improved Feature Selection Algorithm Based on Ant Colony Optimization |
| title_full | An Improved Feature Selection Algorithm Based on Ant Colony Optimization |
| title_fullStr | An Improved Feature Selection Algorithm Based on Ant Colony Optimization |
| title_full_unstemmed | An Improved Feature Selection Algorithm Based on Ant Colony Optimization |
| title_short | An Improved Feature Selection Algorithm Based on Ant Colony Optimization |
| title_sort | improved feature selection algorithm based on ant colony optimization |
| topic | Feature extraction ant colony optimization intrusion detection |
| url | https://ieeexplore.ieee.org/document/8532385/ |
| work_keys_str_mv | AT huijunpeng animprovedfeatureselectionalgorithmbasedonantcolonyoptimization AT chunying animprovedfeatureselectionalgorithmbasedonantcolonyoptimization AT shuhuatan animprovedfeatureselectionalgorithmbasedonantcolonyoptimization AT binghu animprovedfeatureselectionalgorithmbasedonantcolonyoptimization AT zhixinsun animprovedfeatureselectionalgorithmbasedonantcolonyoptimization AT huijunpeng improvedfeatureselectionalgorithmbasedonantcolonyoptimization AT chunying improvedfeatureselectionalgorithmbasedonantcolonyoptimization AT shuhuatan improvedfeatureselectionalgorithmbasedonantcolonyoptimization AT binghu improvedfeatureselectionalgorithmbasedonantcolonyoptimization AT zhixinsun improvedfeatureselectionalgorithmbasedonantcolonyoptimization |