Multigranularity Fuzzy Autoencoder for Discriminative Feature Selection in High-Dimensional Data.

Abstract

Biological datasets, such as gene expression data, often suffer from high dimensionality, containing numerous irrelevant or redundant features that can lead to overfitting and increased computational complexity. Effective feature selection is essential for reducing dimensionality, enhancing model performance, and improving interpretability. While deep neural networks, such as autoencoders, have shown promise in feature selection, their performance often diminishes when confronted with noisy data. To address these challenges, we propose a novel feature selection method that leverages multigranularity fuzzy autoencoders (FAEs). This approach integrates fuzzy theory with autoencoder models to effectively manage noise and outliers in data. The FAE method introduces a feature selection layer that approximates discrete feature selection using continuous probability distributions. To further enhance the discriminative power of the selected features, we incorporate a coarse-grained loss function designed to exploit clustering structures. In addition, intuitionistic fuzzy weights are applied to account for uncertainty by computing membership and nonmembership degrees for each sample, thereby mitigating the impact of noise and outliers. Test results validate the effectiveness of our approach, demonstrating significant improvements over existing feature selection techniques across 20 public datasets and a real-world schizophrenia dataset. These findings highlight the potential of our method to enhance classification accuracy and robustness, particularly in the context of schizophrenia research.