Neural Network Architecture for Synthesis of the Probabilistic Rule Based Classifiers
Dominik Śl ȩ zak, Jakub Wróblewski, Marcin S. Szczuka · Electronic Notes in Theoretical Computer Science · 2003
We introduce a novel neural network architecture, referred to as the normalizing neural network (NNN), where the propagated signals take the form of finite probability distributions. Appropriately tuned NNN can be applied as the compound voting measure while classifying new cases on the basis of approximate decision reducts extracted from the training data. We provide a general scheme of such a classification process, as well as some theoretical issues concerning the NNN construction. We compare the performance of the appropriately learnt NNNs with the fixed voting measures, for some benchmark data sets.