A Method for Fast Feature Selection Utilizing Cross-Similarity within the Context of Fuzzy Relations  

作　　者：Wenchang Yu Xiaoqin Ma Zheqing Zhang Qinli Zhang 

机构地区：[1]School of Big Data and Artificial Intelligence,Chizhou University,Chizhou,247000,China [2]Anhui Education Big Data Intelligent Perception and Application Engineering Research Center,Anhui Provincial Joint Construction Key Laboratory of Intelligent Education Equipment and Technology,Chizhou,247000,China

出　　处：《Computers, Materials & Continua》2025年第4期1195-1218,共24页计算机、材料和连续体(英文)

基　　金：supported by the Anhui Provincial Department of Education University Research Project(2024AH051375);Research Project of Chizhou University(CZ2022ZRZ06);Anhui Province Natural Science Research Project of Colleges and Universities(2024AH051368);Excellent Scientific Research and Innovation Team of Anhui Colleges(2022AH010098).

摘　　要：Feature selection methods rooted in rough sets confront two notable limitations:their high computa-tional complexity and sensitivity to noise,rendering them impractical for managing large-scale and noisy datasets.The primary issue stems from these methods’undue reliance on all samples.To overcome these challenges,we introduce the concept of cross-similarity grounded in a robust fuzzy relation and design a rapid and robust feature selection algorithm.Firstly,we construct a robust fuzzy relation by introducing a truncation parameter.Then,based on this fuzzy relation,we propose the concept of cross-similarity,which emphasizes the sample-to-sample similarity relations that uniquely determine feature importance,rather than considering all such relations equally.After studying the manifestations and properties of cross-similarity across different fuzzy granularities,we propose a forward greedy feature selection algorithm that leverages cross-similarity as the foundation for information measurement.This algorithm significantly reduces the time complexity from O(m2n2)to O(mn2).Experimental findings reveal that the average runtime of five state-of-the-art comparison algorithms is roughly 3.7 times longer than our algorithm,while our algorithm achieves an average accuracy that surpasses those of the five comparison algorithms by approximately 3.52%.This underscores the effectiveness of our approach.This paper paves the way for applying feature selection algorithms grounded in fuzzy rough sets to large-scale gene datasets.

关 键 词：Fuzzy rough sets feature selection cross-similarity fuzzy relations 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]