Improving the accuracy of SVM algorithm in classification problems with PCA method

Abstract

© Springer International Publishing AG 2018. This paper investigates the use of SVM algorithm with PCA method in classification, which is one of the most common task of machine learning. Classification is the problem of identifying to which of a set of categories a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. SVM algorithm can produce accurate and robust classification results on a sound theoretical basis, even when input data are non-monotone and non-linearly separable. So they can help to evaluate more relevant information in a convenient way. PCA method reduces the dimensionality and the maximum number of new variables that can be obtained is equal to the original, with new variables are not correlated with each other. Experimental studies have shown that it is possible to improve the accuracy of SVM classification algorithm using PCA method.