Classification with neural network and SVM via decision tree algorithm

Abstract

This work provides a method for classification using a Support Vector Machine (SVM) via a Decision Tree algorithm. A probabilistic Decision Tree algorithm focusing on large frequency classes (DTPL) is developed. A method for SVM classification (DT_SVM) using Tabu Search (TS) via DTs is developed. In order to reduce the training complexity of the Support Vector Machine (SVM), the DTPL performs partitions that can be treated as clusters. The TS algorithm can provide the ability to approximate the decision boundary of an SVM. Based on DTs, a SVM algorithm is developed to improve the training time of the SVM considering a subset of the cluster’s instances. A Neural Network (NN) is composed of many neurons that are linked together according to a specific network topology. Main characteristics of SVM and NN are presented. Comparison between NN and SVM with two types of kernels show the superiority of the SVM. Simulation results for all the algorithms with different complexity data sets are provided.