Feature Selection Based on a Novel Improved Tree Growth Algorithm
Feature selection plays a significant role in the field of data mining and machine learning to reduce the data dimension, speed up the model building process and improve algorithm performance. Tree growth algorithm (TGA) is a recent proposed population-based metaheuristic, which shows great power of...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2020-02-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/125935159/view |
| _version_ | 1811302345203515392 |
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| author | Changkang Zhong Yu Chen Jian Peng |
| author_facet | Changkang Zhong Yu Chen Jian Peng |
| author_sort | Changkang Zhong |
| collection | DOAJ |
| description | Feature selection plays a significant role in the field of data mining and machine learning to reduce the data dimension, speed up the model building process and improve algorithm performance. Tree growth algorithm (TGA) is a recent proposed population-based metaheuristic, which shows great power of search ability in solving optimization of continuous problems. However, TGA cannot be directly applied to feature selection problems. Also, we find that its efficiency still leave room for improvement. To tackle this problem, in this study, a novel improved TGA (iTGA) is proposed, which can resolve the feature selection problem efficiently. The main contribution includes, (1) a binary TGA is proposed to tackle the feature selection problems, (2) a linearly increasing parameter tuning mechanism is proposed to tune the parameter in TGA, (3) the evolutionary population dynamics (EPD) strategy is applied to improve the exploration and exploitation capabilities of TGA, (4) the efficiency of iTGA is evaluated on fifteen UCI benchmark datasets, the comprehensive results indicate that iTGA can resolve feature selection problems efficiently. Furthermore, the results of comparative experiments also verify the superiority of iTGA compared with other state-of-the-art methods. |
| first_indexed | 2024-04-13T07:26:24Z |
| format | Article |
| id | doaj.art-f6c1cf6ea11b4abdb0d4c3d9c70dc050 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-13T07:26:24Z |
| publishDate | 2020-02-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-f6c1cf6ea11b4abdb0d4c3d9c70dc0502022-12-22T02:56:28ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832020-02-0113110.2991/ijcis.d.200219.001Feature Selection Based on a Novel Improved Tree Growth AlgorithmChangkang ZhongYu ChenJian PengFeature selection plays a significant role in the field of data mining and machine learning to reduce the data dimension, speed up the model building process and improve algorithm performance. Tree growth algorithm (TGA) is a recent proposed population-based metaheuristic, which shows great power of search ability in solving optimization of continuous problems. However, TGA cannot be directly applied to feature selection problems. Also, we find that its efficiency still leave room for improvement. To tackle this problem, in this study, a novel improved TGA (iTGA) is proposed, which can resolve the feature selection problem efficiently. The main contribution includes, (1) a binary TGA is proposed to tackle the feature selection problems, (2) a linearly increasing parameter tuning mechanism is proposed to tune the parameter in TGA, (3) the evolutionary population dynamics (EPD) strategy is applied to improve the exploration and exploitation capabilities of TGA, (4) the efficiency of iTGA is evaluated on fifteen UCI benchmark datasets, the comprehensive results indicate that iTGA can resolve feature selection problems efficiently. Furthermore, the results of comparative experiments also verify the superiority of iTGA compared with other state-of-the-art methods.https://www.atlantis-press.com/article/125935159/viewFeature selectionTree growth algorithmEvolutionary population dynamicsMetaheuristic |
| spellingShingle | Changkang Zhong Yu Chen Jian Peng Feature Selection Based on a Novel Improved Tree Growth Algorithm International Journal of Computational Intelligence Systems Feature selection Tree growth algorithm Evolutionary population dynamics Metaheuristic |
| title | Feature Selection Based on a Novel Improved Tree Growth Algorithm |
| title_full | Feature Selection Based on a Novel Improved Tree Growth Algorithm |
| title_fullStr | Feature Selection Based on a Novel Improved Tree Growth Algorithm |
| title_full_unstemmed | Feature Selection Based on a Novel Improved Tree Growth Algorithm |
| title_short | Feature Selection Based on a Novel Improved Tree Growth Algorithm |
| title_sort | feature selection based on a novel improved tree growth algorithm |
| topic | Feature selection Tree growth algorithm Evolutionary population dynamics Metaheuristic |
| url | https://www.atlantis-press.com/article/125935159/view |
| work_keys_str_mv | AT changkangzhong featureselectionbasedonanovelimprovedtreegrowthalgorithm AT yuchen featureselectionbasedonanovelimprovedtreegrowthalgorithm AT jianpeng featureselectionbasedonanovelimprovedtreegrowthalgorithm |