  Class imbalance is a major problem in many real world classification tasks.
Due to the imbalance in the number of samples, the support vector machine (SVM)
classifier gets biased toward the majority class. Furthermore, these samples
are often observed with a certain degree of noise. Therefore, to remove these
problems we propose a novel fuzzy based approach to deal with class imbalanced
as well noisy datasets. We propose two approaches to address these problems.
The first approach is based on the intuitionistic fuzzy membership, termed as
robust energy-based intuitionistic fuzzy least squares twin support vector
machine (IF-RELSTSVM). Furthermore, we introduce the concept of
hyperplane-based fuzzy membership in our second approach, where the final
classifier is termed as robust energy-based fuzzy least square twin support
vector machine (F-RELSTSVM). By using this technique, the membership values are
based on a projection based approach, where the data points are projected on
the hyperplanes. The performance of the proposed algorithms is evaluated on
several benchmark and synthetic datasets. The experimental results show that
the proposed IF-RELSTSVM and F-RELSTSVM models outperform the baseline
algorithms. Statistical tests are performed to check the significance of the
proposed algorithms. The results show the applicability of the proposed
algorithms on noisy as well as imbalanced datasets.
