A novel two-stage feature selection method based on random forest and improved genetic algorithm for enhancing classification in machine learning
The data acquisition methods are becoming increasingly diverse and advanced, leading to higher data dimensions, blurred classification boundaries, and overfitting datasets, affecting machine learning models’ accuracy. Many studies have sought to improve model performance through feature selection. H...
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| Vydané v: | Scientific reports Ročník 15; číslo 1; s. 16828 - 16 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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14.05.2025
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| ISSN: | 2045-2322, 2045-2322 |
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| Abstract | The data acquisition methods are becoming increasingly diverse and advanced, leading to higher data dimensions, blurred classification boundaries, and overfitting datasets, affecting machine learning models’ accuracy. Many studies have sought to improve model performance through feature selection. However, a single feature selection method has incomplete, unstable, or time-consuming shortcomings. Combining the advantages of various feature selection methods can help overcome these defects. This paper proposes a two-stage feature selection method based on random forest and improved genetic algorithm. First, the importance scores of the random forest are calculated and ranked, and the features are preliminarily eliminated according to the scores, reducing the time complexity of the subsequent process. Then, the improved genetic algorithm is used to search for the global optimal feature subset further. This process introduces a multi-objective fitness function to guide the feature subset, minimizing the number of features in the subset while enhancing classification accuracy. This paper also adds an adaptive mechanism and evolution strategy to improve the loss of population diversity and degeneration in the later stages of iteration, thereby enhancing search efficiency. The experimental results on eight UCI datasets show that the proposed method significantly improves classification performance and has excellent feature selection capability. |
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| AbstractList | The data acquisition methods are becoming increasingly diverse and advanced, leading to higher data dimensions, blurred classification boundaries, and overfitting datasets, affecting machine learning models' accuracy. Many studies have sought to improve model performance through feature selection. However, a single feature selection method has incomplete, unstable, or time-consuming shortcomings. Combining the advantages of various feature selection methods can help overcome these defects. This paper proposes a two-stage feature selection method based on random forest and improved genetic algorithm. First, the importance scores of the random forest are calculated and ranked, and the features are preliminarily eliminated according to the scores, reducing the time complexity of the subsequent process. Then, the improved genetic algorithm is used to search for the global optimal feature subset further. This process introduces a multi-objective fitness function to guide the feature subset, minimizing the number of features in the subset while enhancing classification accuracy. This paper also adds an adaptive mechanism and evolution strategy to improve the loss of population diversity and degeneration in the later stages of iteration, thereby enhancing search efficiency. The experimental results on eight UCI datasets show that the proposed method significantly improves classification performance and has excellent feature selection capability. Abstract The data acquisition methods are becoming increasingly diverse and advanced, leading to higher data dimensions, blurred classification boundaries, and overfitting datasets, affecting machine learning models’ accuracy. Many studies have sought to improve model performance through feature selection. However, a single feature selection method has incomplete, unstable, or time-consuming shortcomings. Combining the advantages of various feature selection methods can help overcome these defects. This paper proposes a two-stage feature selection method based on random forest and improved genetic algorithm. First, the importance scores of the random forest are calculated and ranked, and the features are preliminarily eliminated according to the scores, reducing the time complexity of the subsequent process. Then, the improved genetic algorithm is used to search for the global optimal feature subset further. This process introduces a multi-objective fitness function to guide the feature subset, minimizing the number of features in the subset while enhancing classification accuracy. This paper also adds an adaptive mechanism and evolution strategy to improve the loss of population diversity and degeneration in the later stages of iteration, thereby enhancing search efficiency. The experimental results on eight UCI datasets show that the proposed method significantly improves classification performance and has excellent feature selection capability. The data acquisition methods are becoming increasingly diverse and advanced, leading to higher data dimensions, blurred classification boundaries, and overfitting datasets, affecting machine learning models' accuracy. Many studies have sought to improve model performance through feature selection. However, a single feature selection method has incomplete, unstable, or time-consuming shortcomings. Combining the advantages of various feature selection methods can help overcome these defects. This paper proposes a two-stage feature selection method based on random forest and improved genetic algorithm. First, the importance scores of the random forest are calculated and ranked, and the features are preliminarily eliminated according to the scores, reducing the time complexity of the subsequent process. Then, the improved genetic algorithm is used to search for the global optimal feature subset further. This process introduces a multi-objective fitness function to guide the feature subset, minimizing the number of features in the subset while enhancing classification accuracy. This paper also adds an adaptive mechanism and evolution strategy to improve the loss of population diversity and degeneration in the later stages of iteration, thereby enhancing search efficiency. The experimental results on eight UCI datasets show that the proposed method significantly improves classification performance and has excellent feature selection capability.The data acquisition methods are becoming increasingly diverse and advanced, leading to higher data dimensions, blurred classification boundaries, and overfitting datasets, affecting machine learning models' accuracy. Many studies have sought to improve model performance through feature selection. However, a single feature selection method has incomplete, unstable, or time-consuming shortcomings. Combining the advantages of various feature selection methods can help overcome these defects. This paper proposes a two-stage feature selection method based on random forest and improved genetic algorithm. First, the importance scores of the random forest are calculated and ranked, and the features are preliminarily eliminated according to the scores, reducing the time complexity of the subsequent process. Then, the improved genetic algorithm is used to search for the global optimal feature subset further. This process introduces a multi-objective fitness function to guide the feature subset, minimizing the number of features in the subset while enhancing classification accuracy. This paper also adds an adaptive mechanism and evolution strategy to improve the loss of population diversity and degeneration in the later stages of iteration, thereby enhancing search efficiency. The experimental results on eight UCI datasets show that the proposed method significantly improves classification performance and has excellent feature selection capability. |
| ArticleNumber | 16828 |
| Author | Wang, Hejie Ding, Junyao Xiao, Song Du, Jianchao |
| Author_xml | – sequence: 1 givenname: Junyao surname: Ding fullname: Ding, Junyao organization: School of Telecommunications Engineering, Xidian University – sequence: 2 givenname: Jianchao surname: Du fullname: Du, Jianchao email: jcdu@xidian.edu.cn organization: School of Telecommunications Engineering, Xidian University – sequence: 3 givenname: Hejie surname: Wang fullname: Wang, Hejie organization: School of Telecommunications Engineering, Xidian University – sequence: 4 givenname: Song surname: Xiao fullname: Xiao, Song organization: Beijing Electronic Science and Technology Institute |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40369050$$D View this record in MEDLINE/PubMed |
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| Keywords | Feature selection Random forest Data mining Improved genetic algorithm Machine learning |
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| SubjectTerms | 639/705/117 639/705/258 Algorithms Classification Data acquisition Data mining Evolution Feature selection Genetic algorithms Humanities and Social Sciences Improved genetic algorithm Learning algorithms Machine learning multidisciplinary Random forest Science Science (multidisciplinary) |
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| Title | A novel two-stage feature selection method based on random forest and improved genetic algorithm for enhancing classification in machine learning |
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