A novel algorithm to optimize classification trees

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)

摘要
布赖曼等（1984年）阐述了被称为分类与回归树（Classification and Regression Trees，简称CART）的方法，该方法适用于非参数判别和回归。在本篇论文中，我们提出了一种算法，该算法能够提高分类树的质量，使其超越基于直接评估分割准则的树的质量。该新颖算法通过计算大量可能的树段，而非单一树，递归地选择其中最佳者...

程序名称：MedTree 3.1
目录编号：ADCY_v1_0

问题性质
本问题旨在为特定主题寻找最佳分类树以归入两组之一[1]。最初，用户必须从两组的代表主题中采样一组（充分）大量的特征。如果输入信息中存在简单的行为方案，甚至复杂的关联，则预期将找到优良的树。该算法允许考虑边界条件，以适应分类树的实际应用目的。

CPC软件库中保存的此程序版本
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

本程序已从贝尔法斯特女王大学（1969-2019年）保存的CPC程序库中导入。