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

arXiv: https://arxiv.org/abs/2307.08713 · HTML: https://mlsanity.com/paper/2307.08713/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 48 references that are in the map.
- First posted on arXiv 15 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates](https://mlsanity.com/paper/1305.5029/) (2013)
- [Random vector functional link network: recent developments, applications, and future directions](https://mlsanity.com/paper/2203.11316/) (2022)
- [A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis](https://mlsanity.com/paper/2005.04573/) (2020)
- [Successes and critical failures of neural networks in capturing human-like speech recognition](https://mlsanity.com/paper/2204.03740/) (2022)

## Built on by (most cited first, 13 of 13)

- [Wave-RVFL: A Randomized Neural Network Based on Wave Loss Function](https://mlsanity.com/paper/2408.02824/) (2024)
- [GB-RVFL: Fusion of Randomized Neural Network and Granular Ball Computing](https://mlsanity.com/paper/2409.16735/) (2024)
- [Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference System](https://mlsanity.com/paper/2406.00801/) (2024)
- [GRVFL-MV: Graph Random Vector Functional Link Based on Multi-View Learning](https://mlsanity.com/paper/2409.04743/) (2024)
- [Enhanced Feature Based Granular Ball Twin Support Vector Machine](https://mlsanity.com/paper/2410.05786/) (2024)
- [Randomized based restricted kernel machine for hyperspectral image classification](https://mlsanity.com/paper/2503.05837/) (2025)
- [CAWI: Copula-Aligned Weight Initialization for Randomized Neural Networks](https://mlsanity.com/paper/2605.12580/) (2026)
- [Graph-Embedded Intuitionistic Fuzzy Broad Learning System: A Multi-view Framework](https://mlsanity.com/paper/2607.16728/) (2026)
- [Physics-Informed Broad Learning System: An Efficient Backpropagation-Free Framework for Solving Partial Differential Equations](https://mlsanity.com/paper/2607.25608/) (2026)
- [Uncertainty-Aware Ensemble Deep Randomized Neural Networks for Classification](https://mlsanity.com/paper/2608.10007/) (2026)
- [ECA-BLS: An Efficient Complex-Augmented Broad Learning System](https://mlsanity.com/paper/2608.29763/) (2026)
- [Robust Broad Learning System with Wave Loss for Classification under Data Uncertainty](https://mlsanity.com/paper/2608.29983/) (2026)
- [IFW-BLS: Dual-Robust Broad Learning System with Intuitionistic Fuzzy Wave Loss](https://mlsanity.com/paper/2609.02422/) (2026)

## Abstract (arXiv)

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.