Improving Accuracy of Classification Based on C4.5 Decision Tree Algorithm Using Big Data Analytics

Abstract

C4.5 is an algorithm of decision tree that broadly used classification technique. There are many challenges in the era of big data like size, time, and cost for building a decision tree. Aim of the decision tree construction is to boost up the accuracy on the training data. In predictive modeling, it requires to split the training datasets for this MATLAB is a good choice. Also analysis of data is done easily by decision tree instead of heterogeneous data. In this paper, C4.5 is implemented with the help of MATLAB using four different datasets which provides a confusion matrix in terms of target and output classes. At the end, it compared the features of datasets. The main objective of this research is to boost up the classification accuracy and roll back timing to build a classification model. We have reduced input space using Bhattacharya distance. The proposed method shows better performance for the data file. With the help of BD, improved C4.5 is performing better than original C4.5 in every test case.