The development of computer technology has enhanced the people’s ability to produce and collect data. Data mining techniques can be effectively utilized for analyzing the data to discover hidden knowledge. One of the well known and efficient techniques is decision trees, due to easy understanding structural output. But they may not always be easy to understand due to very big structural output. To overcome this short coming pruning can be used as a key procedure. It removes overusing noisy, conflicting data, so as to have better generalization. However, In pruning the problem of how to make a trade-off between classification accuracy and tree size has not been well solved.

In this paper, firstly we propose a new pruning method aiming on both classification accuracy and tree size. Based upon the method, we introduce a simple decision tree pruning technique, and evaluated the hypothesis–Does our new pruning method yields Better and Compact decision trees? The experimental results are verified by using benchmark datasets from UCI machine learning repository. The results indicate that our new tree pruning method is a feasible way of pruning decision trees.