Nonparallel hyperplane classifiers for multi-category classification

Support vector machines (SVMs) are benchmark developments in the field of machine learning. Recently, various nonparallel hyperplanes classification algorithms (NHCAs) have been proposed, which are comparable in terms of classification accuracy when compared with SVM but are computationally more efficient. All these NHCAs are originally proposed for binary classification problems. Since, most of the real world classification problems deal with multiple classes, these algorithms are extended in multi-category scenario. We present a comparative study of four NHCAs - Twin SVM (TWSVM), Generalized eigenvalue proximal SVM (GEPSVM), Regularized GEPSVM (RegGEPSVM) and Improved GEPSVM (IGEPSVM) for multi-category classification. The multi-category classification algorithms for NHCA classifiers are implemented using One-Against-All (OAA), binary tree-based (BT) and ternary decision structure (TDS) approaches and the experiments are performed on benchmark UCI datasets. The experimental results show that TDS-TWSVM outperforms other algorithms concerning classification accuracy and BT-RegGEPSVM takes minimum time for building the classifier.