International Journal of Computer Science & Engineering Technology (IJCSET)
Decision trees are extensively used in classification, pattern recognition and data mining. Classical decision tree building algorithms Iterative dichotomiser 3 (id3) uses one attribute to test at each internal node, resulting in the decision boundaries being parallel to the axis and builds the tree one node at a time. In this paper, the authors propose where at each internal node a hyperplane is selected based on all attributes, this hyperplane partitions the training set into two disjoint sets. Their method also tries to build most of the tree in a single optimization problem.