  Generalized eigenvalue proximal support vector machine (GEPSVM) has attracted
widespread attention due to its simple architecture, rapid execution, and
commendable performance. GEPSVM gives equal significance to all samples,
thereby diminishing its robustness and efficacy when confronted with real-world
datasets containing noise and outliers. In order to reduce the impact of noises
and outliers, we propose a novel intuitionistic fuzzy generalized eigenvalue
proximal support vector machine (IF-GEPSVM). The proposed IF-GEPSVM assigns the
intuitionistic fuzzy score to each training sample based on its location and
surroundings in the high-dimensional feature space by using a kernel function.
The solution of the IF-GEPSVM optimization problem is obtained by solving a
generalized eigenvalue problem. Further, we propose an intuitionistic fuzzy
improved GEPSVM (IF-IGEPSVM) by solving the standard eigenvalue decomposition
resulting in simpler optimization problems with less computation cost which
leads to an efficient intuitionistic fuzzy-based model. We conduct a
comprehensive evaluation of the proposed IF-GEPSVM and IF-IGEPSVM models on UCI
and KEEL datasets. Moreover, to evaluate the robustness of the proposed
IF-GEPSVM and IF-IGEPSVM models, label noise is introduced into some UCI and
KEEL datasets. The experimental findings showcase the superior generalization
performance of the proposed models when compared to the existing baseline
models, both with and without label noise. Our experimental results, supported
by rigorous statistical analyses, confirm the superior generalization abilities
of the proposed IF-GEPSVM and IF-IGEPSVM models over the baseline models.
Furthermore, we implement the proposed IF-GEPSVM and IF-IGEPSVM models on the
USPS recognition dataset, yielding promising results that underscore the
models' effectiveness in practical and real-world applications.
