Intuitionistic Fuzzy Broad Learning System: Enhancing Robustness Against
  Noise and Outliers
M. Sajid A. K. Malik M. Tanveer
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.
It consists of feature learning and enhancement segments, working together to
extract intricate features from input data. 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 the fuzzy BLS (F-BLS) model, which assigns a fuzzy membership value to
each training point to reduce the influence of noises and outliers. In
assigning the membership value, the F-BLS model solely considers the distance
from samples to the class center in the original feature space without
incorporating the extent of non-belongingness to a class. We further propose a
novel BLS based on intuitionistic fuzzy theory (IF-BLS). The proposed IF-BLS
utilizes intuitionistic fuzzy numbers based on fuzzy membership and
non-membership values to assign scores to training points in the
high-dimensional feature space by using a kernel function. We evaluate the
performance of proposed F-BLS and IF-BLS models on 44 UCI benchmark datasets
across diverse domains. Furthermore, Gaussian noise is added to some UCI
datasets to assess the robustness of the proposed F-BLS and IF-BLS models.
Experimental results demonstrate superior generalization performance of the
proposed F-BLS and IF-BLS models compared to baseline models, both with and
without Gaussian noise. Additionally, we implement the proposed F-BLS and
IF-BLS models on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset,
and promising results showcase the models effectiveness in real-world
applications. The proposed methods offer a promising solution to enhance the
BLS frameworks ability to handle noise and outliers.