Constructing neural networks for multiclass-discretization based on information entropy

Abstract

Cios and Liu (1992) proposed an entropy-based method to generate the architecture of neural networks for supervised two-class discretization. For multiclass discretization, the inter-relationship among classes is reduced to a set of binary relationships, and an independent two-class subnetwork is created for each binary relationship. This two-class-based method ends up with the disability of sharing hidden nodes among different classes and a low recognition rate. We keep the interrelationship among classes when training a neural network. Entropy measure is considered in a global sense, not locally in each independent subnetwork. Consequently, our method allows hidden nodes and layers to be shared among classes, and presents higher recognition rates than the two-class-based method.