Enhancing robustness and efficiency of least square twin SVM via granular computing

Abstract

In the domain of machine learning, least square twin support vector machine (LSTSVM) stands out as one of the state-of-the-art classification model. However, LSTSVM is not without its limitations. It exhibits sensitivity to noise and outliers, fails to adequately incorporate the structural risk minimization (SRM) principle, and often demonstrates instability under resampling scenarios. Moreover, its computational complexity and reliance on matrix inversions hinder the efficient processing of large datasets. As a remedy to the aforementioned challenges, we propose the robust granular ball LSTSVM (GBLSTSVM). GBLSTSVM is trained using granular balls instead of original data points. The core of a granular ball is found at its center, where it encapsulates all the pertinent information of the data points within the ball of specified radius. To improve scalability and efficiency, we further introduce the large-scale GBLSTSVM (LS-GBLSTSVM), which incorporates the SRM principle through regularization terms. Experiments are performed on UCI, KEEL, and NDC benchmark dataset demonstrate that both the proposed GBLSTSVM and LS-GBLSTSVM models consistently outperform the baseline models. The source code of the proposed GBLSTSVM model is available at https://github.com/mtanveer1/GBLSTSVM.