An Adapted Ant Colony Optimization for Feature Selection
ABSTRACTAs information technologies evolve, they generate vast and ever-expanding datasets. This wealth of high-dimensional data presents challenges, including increased computational demands and difficulties in extracting valuable insights. The aim of feature selection is to address this complexity...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2335098 |
| _version_ | 1797230179453501440 |
|---|---|
| author | Duygu Yilmaz Eroglu Umut Akcan |
| author_facet | Duygu Yilmaz Eroglu Umut Akcan |
| author_sort | Duygu Yilmaz Eroglu |
| collection | DOAJ |
| description | ABSTRACTAs information technologies evolve, they generate vast and ever-expanding datasets. This wealth of high-dimensional data presents challenges, including increased computational demands and difficulties in extracting valuable insights. The aim of feature selection is to address this complexity by reducing data dimensions with minimal information loss. Our proposed feature selection approach, the Feature Selection via Ant Colony Optimization algorithm, employs heuristic distance directly in its probability function, instead of using its inverse. The algorithm bypasses the need for sub-attribute sets, running multiple iterations to create a frequency order list from the collected routes, which informs feature importance. The efficacy of this technique has been validated through comparative experiments with other methods from scientific literature. To ensure fairness, these experiments used identical datasets, data partitioning strategies, classifiers, and performance metrics. Initially, the algorithm was compared with fifteen different algorithms, and subsequently benchmarked against three selected methods. The impact of feature selection on classification performance was statistically verified through comparisons before and after the feature selection process. Convergence performance of the proposed method has also been evaluated. Our findings robustly support the efficacy of the introduced approach in managing complex, multidimensional data effectively. |
| first_indexed | 2024-04-24T15:24:22Z |
| format | Article |
| id | doaj.art-4581abe35d8641dcba28c5368b68b7fe |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-04-24T15:24:22Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-4581abe35d8641dcba28c5368b68b7fe2024-04-02T06:43:58ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2335098An Adapted Ant Colony Optimization for Feature SelectionDuygu Yilmaz Eroglu0Umut Akcan1Department of Industrial Engineering, Bursa Uludag University, Bursa, TurkeyGraduate School of Natural and Applied Sciences, Industrial Engineering Program, Bursa Uludag University, Bursa, TurkeyABSTRACTAs information technologies evolve, they generate vast and ever-expanding datasets. This wealth of high-dimensional data presents challenges, including increased computational demands and difficulties in extracting valuable insights. The aim of feature selection is to address this complexity by reducing data dimensions with minimal information loss. Our proposed feature selection approach, the Feature Selection via Ant Colony Optimization algorithm, employs heuristic distance directly in its probability function, instead of using its inverse. The algorithm bypasses the need for sub-attribute sets, running multiple iterations to create a frequency order list from the collected routes, which informs feature importance. The efficacy of this technique has been validated through comparative experiments with other methods from scientific literature. To ensure fairness, these experiments used identical datasets, data partitioning strategies, classifiers, and performance metrics. Initially, the algorithm was compared with fifteen different algorithms, and subsequently benchmarked against three selected methods. The impact of feature selection on classification performance was statistically verified through comparisons before and after the feature selection process. Convergence performance of the proposed method has also been evaluated. Our findings robustly support the efficacy of the introduced approach in managing complex, multidimensional data effectively.https://www.tandfonline.com/doi/10.1080/08839514.2024.2335098 |
| spellingShingle | Duygu Yilmaz Eroglu Umut Akcan An Adapted Ant Colony Optimization for Feature Selection Applied Artificial Intelligence |
| title | An Adapted Ant Colony Optimization for Feature Selection |
| title_full | An Adapted Ant Colony Optimization for Feature Selection |
| title_fullStr | An Adapted Ant Colony Optimization for Feature Selection |
| title_full_unstemmed | An Adapted Ant Colony Optimization for Feature Selection |
| title_short | An Adapted Ant Colony Optimization for Feature Selection |
| title_sort | adapted ant colony optimization for feature selection |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2335098 |
| work_keys_str_mv | AT duyguyilmazeroglu anadaptedantcolonyoptimizationforfeatureselection AT umutakcan anadaptedantcolonyoptimizationforfeatureselection AT duyguyilmazeroglu adaptedantcolonyoptimizationforfeatureselection AT umutakcan adaptedantcolonyoptimizationforfeatureselection |