Linear discriminant classification tree: A user-driven multicriteria classification method

Abstract

Todeschini, R. and Marengo, E., 1992. Linear discriminant classification tree: a user-driven multi-criteria classification method. Chemometrics and Intelligent Laboratory Systems . 16:25–35. A classification method, linear discriminant classification tree (LDCT), has been developed with particular attention to problem-driven solutions. It consists in the joint application of linear discriminant analysis (LDA) and classification tree methods. The population of each node is partitioned into two groups and classified using LDA which allows the introduction of multivariate binary classifiers. Thus the resulting classification trees are usually characterized by low complexity and ready interpretability. Several different trees can be obtained from the same data set: each tree can be cross-validated and a choice made on the basis of different criteria. This flexibility makes LDCT a really problem-driven classification method. Eight real data sets were used to test the method, and in all cases the results were good.