A Robust Algorithm for Classification Using Decision Trees

Abstract

Decision trees algorithms have been suggested in the past for classification of numeric as well as categorical attributes. SLIQ algorithm was proposed (Mehta et al., 1996) as an improvement over ID3 and C4.5 algorithms (Quinlan, 1993). Elegant Decision Tree Algorithm was proposed (Chandra et al. 2002) to improve the performance of SLIQ. In this paper a novel approach has been presented for the choice of split value of attributes. The issue of reducing the number of split points has been addressed. It has been shown on various datasets taken from UCI machine learning data repository that this approach gives better classification accuracy as compared to C4.5, SLIQ and Elegant Decision Tree Algorithm (EDTA) and at the same time the number of split points to be evaluated is much less compared to that of SLIQ and EDTA