An Optimized Decision Trees Approach for Knowledge Discovery Using Orthogonal Radom Matrix Projection with Outlier Detection

Abstract

In data mining one of the challenging problems is how to handle high dimensional and complex datasets. Decision trees when applied to high dimensional and complex datasets produce decision trees which are very complex in nature and thereby reducing generalization. To address this issue we propose an algorithm know as Radom Matrix Projection with Outlier Detection (RMPOD). The proposed algorithm is validated on 24 UCI datasets against accuracy and tree size metrics. The results of the proposed algorithm with compared algorithm suggest an improvement in accuracy and tree size for better generalization.