System for constructing decision tree classifiers using structure-driven induction

摘要

A computer-implemented apparatus and method for designing decision tree classifiers for use in artificial applications. A tree structure of fixed size with empty internal nodes, i.e. nodes without any splitting function, and labeled terminal nodes is first constructed. Using a collection of training vectors of known classification, a neural learning scheme combining backpropagation and soft competitive learning is then used to simultaneously determine the splits for each decision tree node. Compact trees are generated that have multifeature splits at each internal node which are determined on global rather than local basis. The computer-implemented apparatus and method consequently produces decision trees yielding better classification and interpretation of the underlying relationships in the data.