A Searching Method of Candidate Segmentation Point in SPRINT Classification

Abstract

SPRINT algorithm is a classical algorithm for building a decision tree that is a widely used method of data classification. However, the SPRINT algorithm has high computational cost in the calculation of attribute segmentation. In this paper, an improved SPRINT algorithm is proposed, which searches better candidate segmentation point for the discrete and continuous attributes. The experiment results demonstrate that the proposed algorithm can reduce the computation cost and improve the efficiency of the algorithm by improving the segmentation of continuous attributes and discrete attributes.