Bipolar fuzzy based least squares twin bounded support vector machine

Abstract

Data classification is a key domain of research in real-world applications. One of the big challenges of real-world data classification is to tackle the presence of noise and outliers. In this paper, we handle the computational cost of Bipolar evaluation pairs score value based support vector machine model by formulating two efficient variants, referred to as bipolar fuzzy least squares support vector machines and bipolar fuzzy least squares twin bounded support vector machines, in which the score value is obtained as a bipolar evaluation pair with membership and non-membership functions. The solution of primal problems is attained by solving a system of linear equations that leads to less training time complexity unlike other fuzzy based twin models obtain the solution by handling two quadratic programming problems. Furthermore, our proposed models minimize the impact of noise in the data and facilitate the separation of support vectors from noise. The proposed works have been computationally analyzed on many publicly available real-world benchmark datasets as well as simulated artificial datasets in the non-linear case for different significant levels of noise, namely 0% (noise-free) and 5% (noise-corrupted). In comparison to other similar models, our suggested model is showing phenomenal generalization performance by controlling the negative impact of noise and needs substantially less training time. The results of the proposed models are also validated using quality metrics including AUC, F1-score, G-mean, and Precision Predictive Value.