A New Permutation-Based Method for Ranking and Selecting Group Features in Multiclass Classification
- Title
- A New Permutation-Based Method for Ranking and Selecting Group Features in Multiclass Classification
- Author
- 김병훈
- Keywords
- group feature; feature selection; permutation; multiclass classification
- Issue Date
- 2024-04-09
- Publisher
- MDPI
- Citation
- APPLIED SCIENCES-BASEL, v. 14, NO 8, Page. 1-16
- Abstract
- The selection of group features is a critical aspect in reducing model complexity by choosing the most essential group features, while eliminating the less significant ones. The existing group feature selection methods select a set of important group features, without providing the relative importance of all group features. Moreover, few methods consider the relative importance of group features in the selection process. This study introduces a permutation-based group feature selection
approach specifically designed for high-dimensional multiclass datasets. Initially, the least absolute shrinkage and selection operator (lasso) method was applied to eliminate irrelevant individual features within each group feature. Subsequently, the relative importance of the group features was computed using a random-forest-based permutation method. Accordingly, the process selected the highly significant group features. The performance of the proposed method was evaluated using
machine learning algorithms and compared with the performance of other approaches, such as group lasso. We used real-world, high-dimensional, multiclass microarray datasets to demonstrate its effectiveness. The results highlighted the capability of the proposed method, which not only selected significant group features but also provided the relative importance and ranking of all group features. Furthermore, the proposed method outperformed the existing method in terms of accuracy and
F1 score.
- URI
- https://information.hanyang.ac.kr/#/eds/detail?an=edsdoj.b2025f9446d44e21954080c41994b265&dbId=edsdojhttps://repository.hanyang.ac.kr/handle/20.500.11754/190151
- ISSN
- 2076-3417
- DOI
- DOI:10.3390/app14083156
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > INDUSTRIAL AND MANAGEMENT ENGINEERING(산업경영공학과) > Articles
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