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RFAE: A high-robust feature selector based on fractal autoencoder

文献类型：期刊论文

作者Ou, Jingfeng2,3; Li, Jiawei3; Xia, Zhiliang3; Dai, Shurui3; Guo, Yan4; Jiang, Limin4; Tang, Jijun1,3
刊名EXPERT SYSTEMS WITH APPLICATIONS
出版日期2025-08-01
卷号285页码:13
关键词Feature selection Model robustness Weight exponentiation Dual-network mechanism
ISSN号0957-4174
DOI10.1016/j.eswa.2025.127519
英文摘要Feature selection aims to consistently identify an optimal subset of features that effectively represents entire dataset or enhances performance in downstream tasks. While deep learning-based approaches have made significant progress in feature selection, they continue to face key challenges, including instability in selected features, limited receptive fields in feature-selection layers due to architectural constraints, suboptimal utilization of available sample information. To address these limitations, we propose the Robust Fractal Autoencoder (RFAE), an enhanced variant of the Fractal Autoencoder (FAE) designed to improve feature selection stability and adaptability. RFAE introduces three critical advancements: 1) Novel utilization of weight exponentiation to rectify the concern of FAE selecting a reduced number of features than designated. 2) Adoption of a dynamic and tailored strategy to optimize feature selection weights during the training process. 3) Introduction of a optional classification module, facilitating extension to supervised feature selection scenarios. We systematically evaluate RFAE against 14 established feature selection methods. Our experiments span 14 publicly available benchmark datasets, a large-scale GEO gene expression dataset, and a synthetic dataset with known ground-truth features. The results demonstrate that RFAE consistently selects features that achieve lower reconstruction errors while ensuring higher stability across repeated experiments, highlighting its robustness and effectiveness in feature selection tasks.
资助项目National Natural Science Foundation of China[U24A20257] ; Shenzhen Science and Technology gram[JCYJ20241202130212016]
WOS研究方向Computer Science ; Engineering ; Operations Research & Management Science
语种英语
WOS记录号WOS:001492651700002
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/42397]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Jiang, Limin; Tang, Jijun
作者单位1.Chinese Acad Sci, Ctr High Performance Comp, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China
2.Southern Univ Sci & Technol, Coll Engn, Shenzhen 518055, Peoples R China
3.Shenzhen Univ Adv Technol, Fac Comp Sci & Control Engn, Shenzhen 518107, Peoples R China
4.Univ Miami, Dept Publ Hlth Sci, Miami, FL 33136 USA
推荐引用方式
GB/T 7714		 Ou, Jingfeng,Li, Jiawei,Xia, Zhiliang,et al. RFAE: A high-robust feature selector based on fractal autoencoder[J]. EXPERT SYSTEMS WITH APPLICATIONS,2025,285:13.
APA Ou, Jingfeng.,Li, Jiawei.,Xia, Zhiliang.,Dai, Shurui.,Guo, Yan.,...&Tang, Jijun.(2025).RFAE: A high-robust feature selector based on fractal autoencoder.EXPERT SYSTEMS WITH APPLICATIONS,285,13.
MLA Ou, Jingfeng,et al."RFAE: A high-robust feature selector based on fractal autoencoder".EXPERT SYSTEMS WITH APPLICATIONS 285(2025):13.

入库方式： OAI收割

来源：计算技术研究所

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