Enhancing gene selection in DNA microarray data using fuzzy–rough sets

doi:10.1007/s41060-026-01310-7

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Enhancing gene selection in DNA microarray data using fuzzy–rough sets

2026-10-03

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Arla Gopala Krishna

Govind Mohan Shah

Krishna Siva Prasad Mudigonda

Pandu Sowkuntla *

DOI：10.1007/s41060-026-01310-7

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Abstract

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Gene selection is a critical task in analyzing high-dimensional DNA microarray data, where a vast number of genes are measured against a limited number of samples. Identifying the most informative subset of genes not only enhances classification accuracy but also reduces computational complexity, particularly in applications like cancer diagnosis and tumor classification. Fuzzy–rough set theory is an effective method for feature selection, capable of efficiently handling data imprecision and uncertainty during the selection process. This paper proposes a fuzzy–rough set-based gene-selection method (FRGS) that combines three steps: an early-accept forward selection, positive-region removal, and a backward elimination phase. Together, these steps limit redundant evaluations and remove unnecessary genes while relying on the fuzzy–rough dependency measure to guide the search. FRGS is evaluated on nine benchmark microarray datasets and compared with existing state-of-the-art methods. On every dataset, FRGS shortens selection time and preserves or slightly improves classification accuracy. Because the algorithm works directly on raw expression values and requires no discretizations or specialized parameter tuning, it can be applied readily in laboratory settings and large-scale gene-expression studies.

Keywords:

Gene selection

DNA microarray

Fuzzy–rough sets

High dimensionality

Feature selection