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

doi:10.1016/j.eswa.2025.127519

	RFAE: A high-robust feature selector based on fractal autoencoder
	Ou, Jingfeng 1,2; Li, Jiawei 2; Xia, Zhiliang 2; Dai, Shurui 2; Guo, Yan 3; Jiang, Limin 3; Tang, Jijun 2,4
	2025-08-01
发表期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
卷号	285 页码:13
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.
关键词	Feature selection Model robustness Weight exponentiation Dual-network mechanism
DOI	10.1016/j.eswa.2025.127519
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号	WOS:001492651700002
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42398
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.Southern Univ Sci & Technol, Coll Engn, Shenzhen 518055, Peoples R China 2.Shenzhen Univ Adv Technol, Fac Comp Sci & Control Engn, Shenzhen 518107, Peoples R China 3.Univ Miami, Dept Publ Hlth Sci, Miami, FL 33136 USA 4.Chinese Acad Sci, Ctr High Performance Comp, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China
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.