Experiments with ordering attributes for efficient connectionist system development

Abstract

The authors introduce an algorithm for selection and ordering of input attributes based on a generalization to a fuzzy case of the notion of conditional entropy. The algorithm is relatively computationally inexpensive and efficient, as was demonstrated in a number of experiments that are reported. The experimental results support the observation that preselection and ordering of a small number of effective input features constitute an important factor in the development of efficient neural network classifiers.<>