High-dimensional datasets often contain redundant and highly correlated features that increase computational complexity and reduce model interpretability, making effective redundancy-aware feature selection an important challenge for fuzzy–rough learning. This paper proposes a Kohonen Self-Organizing Map-guided Fuzzy-Rough Feature Selection (KFRFS) method that enhances dependency-based fuzzy–rough feature selection by explicitly balancing feature relevance and structural redundancy during reduct construction. The proposed approach first organizes conditional attributes into topology-preserving groups using a Kohonen Self-Organizing Map trained on the transposed decision system and then integrates fuzzy–rough dependency gain with a KSOM-guided redundancy penalty through a unified scoring function within a greedy forward search framework. Extensive experiments on 17 benchmark datasets, including low-, medium-, and high-dimensional microarray datasets, demonstrate that KFRFS consistently produces compact approximate reducts while maintaining or improving classification performance compared with several state-of-the-art fuzzy–rough feature selection methods. Parameter sensitivity, ablation, cumulative dependency, statistical significance, and ranking analyses further show that the proposed redundancy-control mechanism provides a practical trade-off between dependency maximization and redundancy suppression, yielding a balanced relationship between reduct compactness and predictive accuracy. These results suggest that KFRFS can serve as a topology-aware extension of dependency-based fuzzy–rough feature selection for obtaining compact feature subsets in high-dimensional data analysis.