title: 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.
authors.affiliation: Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung.
authors.forename: S J
authors.initials: SJ
authors.lastname: Lee
authors.forename: M T
authors.initials: MT
authors.lastname: Jone
authors.forename: H L
authors.initials: HL
authors.lastname: Tsai