On Improving Efficiency of SLIQ Decision Tree Algorithm

Decision trees have been widely used for classification in Data mining. Number of decision tree algorithms has been developed in the past. The SLIQ algorithm 2 was developed with an aim to reduce diversity of the decision tree at each split. However the number of split points which needs to be examined while building the decision tree becomes enormous as the SLIQ algorithm evaluates Gini Index at every successive midpoint of attribute values. The paper proposes a novel approach to tackle this problem by reducing the number of split points to a large extent in order to improve the performance of SLIQ algorithm. The improved performance is shown on large number of benchmark datasets taken from UCI machine learning repository.