@Article{cmc.2025.060833,
AUTHOR = {Wenchang Yu, Xiaoqin Ma, Zheqing Zhang, Qinli Zhang},
TITLE = {A Method for Fast Feature Selection Utilizing Cross-Similarity within the Context of Fuzzy Relations},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {83},
YEAR = {2025},
NUMBER = {1},
PAGES = {1195--1218},
URL = {http://www.techscience.com/cmc/v83n1/60104},
ISSN = {1546-2226},
ABSTRACT = {Feature selection methods rooted in rough sets confront two notable limitations: their high computational 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(m<sup>2</sup>n<sup>2</sup>) to O(mn<sup>2</sup>). 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.},
DOI = {10.32604/cmc.2025.060833}
}