A method to build classification and regression trees

Abstract

An adaptable structure to build a classification tree is presented. From such structure different existing classification trees can be obtained, but also we can build new ones, and compare the results of different trees (classification error, tree size, number of levels or other defined criteria). We use the adaptable scheme to emulate ID3, C4.5 and M5 trees, but also create a new tree (called general tree), and results obtained shows that we can obtain the same results with the original trees, and for the case of the general tree, its results are very close to the better classifier tree of the three studied.