Continuous ID3 algorithm with fuzzy entropy measures

Abstract

Fuzzy entropy measures are used to obtain a quick convergence of a continuous ID3 (CID3) algorithm proposed by K.J. Cios and N. Liu (1991), which allows for self-generation of a hierarchical feedforward neural network architecture by converting decision trees into hidden layers of a neural network. To demonstrate the learning capacity of the fuzzy version of the CID3 algorithm, it was tested on difficult spiral data consisting of 192 points, with 96 points for each spiral. One spiral is generated as a reflection of another, making the problem highly not linearly separable. A remarkable decrease in convergence time is achieved by using a fuzzy entropy measure with generalized Dombi operations.<>