Combining Decision Trees using Systematic Patterns

Tree ensemble or combining methods that use re-sampling technique have been highlighted recently in Statistical classification and Data mining. In this paper, we propose a new ensemble method in decision trees that utilizes systematic patterns of classification. The new method improved the prediction accuracy of a single decision tree algorithm. It is also observed that this method performs reasonably well with fewer number of re-samples compared to the popular Bagging or Boosting methods. An experiment with real dataset is carried out to see the performance of the new method.