An Optimized Formulation of Decision Tree Classifier

Abstract

An effective input dataset, valid pattern-spotting ability, good discovered pattern evaluation is required in order to analyze, predict and discover previously unknown knowledge from a large data set. The criteria of significance, novelty and usefulness need to be fulfilled in order to evaluate the performance of the prediction and classification of data. Thankfully data mining, an important step in this process of knowledge discovery extract hidden and non-trivial information from raw data through useful methods such as decision tree classification. But due to the enormous size, high-dimensionality and heterogeneous nature of the data sets, the traditional decision tree classification algorithms sometimes do not perform well in terms of computation time. This paper proposes a framework that uses a parallel strategy to optimize the performance of decision tree induction and cross-validation in order to classify data. Moreover, an existing pruning method is incorporated with our framework to overcome the overfitting problem and enhancing generalization ability along with reducing cost and structural complexity. Experiments on ten benchmark data sets suggest significant improvement in computation time and better classification accuracy by optimizing the classification framework.