Abstract
Least square twin support vector machine is an emerging learning method applied in classification problem. This paper present a tree-based least square twin support vector machine (T-LSTWSVM) for classification. Classification procedure depends on the correlation of input feature as well as output feature. UCI benchmark data sets are used to evaluate the test set performance of tree-based least square twin support vector machine (T-LSTWSVM) classifiers with multiple kernel functions such as linear, polynomial and radial basis function (RBF) kernels. This method applies on two main types of classification problems such as binary class problem as well as multi-class problem. The evaluation and accuracy is calculated in terms of distance metric. It was observed that multi-class classification problem performed excellently by tree-based method.
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