EFFICIENT CLASSIFICATION OF BIG DATA USING VFDT (VERY FAST DECISION TREE)

Sourav Roy, Brina Patel, Samruddhi Purandare, Minal Kucheria

Abstract: Decision Tree learning algorithms have been able to capture knowledge successfully. Decision Trees are best considered when instances are described by attribute-value pairs and when the target function has a discrete value. The main task of these decision trees is to use inductive methods to the given values of attributes of an unknown object and determine an appropriate classification by applying decision tree rules. Decision Trees are very effective forms to evaluate the performance and represent the algorithms because of their robustness, simplicity, capability of handling numerical and categorical data, ability to work with large datasets and comprehensibility to a name a few. There are various decision tree algorithms available like ID3, CART, C4.5, VFDT, QUEST, CTREE, GUIDE, CHAID, CRUISE, etc. In this paper a comparative study on three of these popular decision tree algorithms - (Iterative Dichotomizer 3), C4.5 which is an evolution of ID3 and VFDT (Very Fast Decision Tree has been made. An empirical study has been conducted to compare C4.5 and VFDT in terms of accuracy and execution time and various conclusions have been drawn.

Keywords: Decision tree, ID3, C4.5, VFDT, Information Gain, Gain Ratio, Gini Index, Over-fitting

DOI: https://doi.org/10.15623/ijret.2016.0501019