Self-organizing neural tree networks

Automatic pattern classification is a very important field of artificial intelligence. For these kind of tasks different techniques have been used. In this work a combination of decision trees and self-organizing neural networks is presented as an alternative to attack the problem. For the construction of these trees growth processes are applied. In these processes, the evaluation of classification efficiency of one or several nodes in different configurations is necessary in order to take decisions to optimize the structure and performance of the self-organizing neural tree net. In order to perform this task a group of coefficients that quantify the efficiency is defined and a growth algorithm based on these coefficients is developed. In the tests, a comparison with other classification methods, using cross-validation methods with real and artificial databases, is carried out