Efficient Decision Tree Based Data Selection and Support Vector Machine Classification

Today' real world data bases witnessed significant increase in the amount of data in digital format, due to the widespread use of datasets and storage system. There is a need to developing fast and highly accurate algorithms to automatically classify large data. It becomes a vital part of the machine learning and knowledge discovery. The main intention of this paper is however data sizes increases, our proposed method make faster computation and scalable machine learning algorithm is used to learn faster from the labelled training data. Due to its strong mathematical background and theoretical foundation and good generalization performance, Support Vector Machine (SVM) Classification becomes more feasible options for large datasets. A major research goal of SVM is to improve the speed in training and testing phase. In this paper We introduce a proposed algorithm to speed up the training time of SVM is presented. It is highly accurate classification method. However SVM classifiers suffer from slow processing, when training with a large set of data tuples. Our novel approach selects a small representative amount of data from large datasets to enhance training time of SVM. This method uses an induction tree to reduce the training dataset for SVM classification, it generate faster results with improving accuracy rates than the current SVM implementations. (c) 2017 Elsevier Ltd. All rights reserved.