A novel algorithm to optimize classification trees

2019-12-06T07:43:41Z (GMT) by Martin Kröger Bernd Kröger
Abstract Breiman et al. (1984) expounded a method called Classification and Regression Trees, or CART, which is of use for nonparametric discrimination and regression. In this paper we present an algorithm which is able to increase the quality of classification trees beyond the quality of trees, which are based on direct evaluation of a splitting criterion. The novel algorithm calculates a large number of possible segments of trees instead of a single tree, and recursively selects the best of these pa... Title of program: MedTree 3.1 Catalogue Id: ADCY_v1_0 Nature of problem The problem is to find best trees of classification for a specific subject to one of two groups [1]. Initially, a set of features for a (sufficient) large number of representative subjects from both groups must be sampled by the user. A good tree is expected to be found if there exist simple schemes of behaviour, or even complex correlations within the input information. The algorithm allows to take into account boundary conditions, to fit the practical purpose of the classification tree. Versions of this program held in the CPC repository in Mendeley Data ADCY_v1_0; MedTree 3.1; 10.1016/0010-4655(96)00002-1 ADCY_v2_0; MedTree 4.1; 10.1016/S0010-4655(96)00123-3 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)