  Feature selection is a vital technique in machine learning, as it can reduce
computational complexity, improve model performance, and mitigate the risk of
overfitting. However, the increasing complexity and dimensionality of datasets
pose significant challenges in the selection of features. Focusing on these
challenges, this paper proposes a cascaded two-stage feature clustering and
selection algorithm for fuzzy decision systems. In the first stage, we reduce
the search space by clustering relevant features and addressing inter-feature
redundancy. In the second stage, a clustering-based sequentially forward
selection method that explores the global and local structure of data is
presented. We propose a novel metric for assessing the significance of
features, which considers both global separability and local consistency.
Global separability measures the degree of intra-class cohesion and inter-class
separation based on fuzzy membership, providing a comprehensive understanding
of data separability. Meanwhile, local consistency leverages the fuzzy
neighborhood rough set model to capture uncertainty and fuzziness in the data.
The effectiveness of our proposed algorithm is evaluated through experiments
conducted on 18 public datasets and a real-world schizophrenia dataset. The
experiment results demonstrate our algorithm's superiority over benchmarking
algorithms in both classification accuracy and the number of selected features.
