Intuitionistic Fuzzy Broad Learning System: Enhancing Robustness Against Noise and Outliers

TL;DR

This paper introduces fuzzy and intuitionistic fuzzy broad learning systems that improve robustness against noise and outliers in data classification, demonstrating superior performance on benchmark datasets and Alzheimer's diagnosis tasks.

Contribution

The paper proposes novel fuzzy and intuitionistic fuzzy extensions to the broad learning system, enhancing its robustness to noise and outliers in real-world datasets.

Findings

F-BLS and IF-BLS outperform baseline models in noisy conditions.

Models show superior generalization on benchmark datasets.

Effective in diagnosing Alzheimer's disease.

Abstract

In the realm of data classification, broad learning system (BLS) has proven to be a potent tool that utilizes a layer-by-layer feed-forward neural network. However, the traditional BLS treats all samples as equally significant, which makes it less robust and less effective for real-world datasets with noises and outliers. To address this issue, we propose fuzzy broad learning system (F-BLS) and the intuitionistic fuzzy broad learning system (IF-BLS) models that confront challenges posed by the noise and outliers present in the dataset and enhance overall robustness. Employing a fuzzy membership technique, the proposed F-BLS model embeds sample neighborhood information based on the proximity of each class center within the inherent feature space of the BLS framework. Furthermore, the proposed IF-BLS model introduces intuitionistic fuzzy concepts encompassing membership, non-membership, and score value functions. IF-BLS strategically considers homogeneity and heterogeneity in sample neighborhoods in the kernel space. We evaluate the performance of proposed F-BLS and IF-BLS models on UCI benchmark datasets with and without Gaussian noise. As an application, we implement the proposed F-BLS and IF-BLS models to diagnose Alzheimer's disease (AD). Experimental findings and statistical analyses consistently highlight the superior generalization capabilities of the proposed F-BLS and IF-BLS models over baseline models across all scenarios. The proposed models offer a promising solution to enhance the BLS framework's ability to handle noise and outliers. The source code link of the proposed model is available at https://github.com/mtanveer1/IF-BLS.