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Classification is a widely-used learning method in machine learning. The task of classification consists of training samples for which the class labels are available. On the basis of such training samples, a classifier learns a rule for predicting an unseen sample (ShalevShwartz & Ben-David, 2014). To do a classification task, many algorithms have been proposed in machine learning literature such as Artificial Neural Network (ANN), Support Vector Machine (SVM), K-nearest neighbors (KNN), and Decision Trees. Among these classification algorithms, SVM classifier has relatively better prediction accuracy and generalization (Kotsiantis, Zaharakis, & Pintelas, 2007). The main idea of SVM is to find the optimal separating hyperplane with the largest margin between the two classes. Figure 1 shows the geometric illustration of SVM classifier.

LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier