Pruning with interval arithmetic perceptron

In this paper we present a training algorithm for the interval arithmetic perceptron (IAP), i.e. a perceptron which uses interval weights, and describe its use in input pruning. The algorithm is based on the consideration that a zero-value can be assigned to a weight corresponding to an interval with a negative lower value and a positive upper value. Our procedure has been tested on Iris, Breast Cancer and Sonar databases, showing that many input features are unnecessary for a satisfactory classification performance. Comparison with a well-established feature screening method showed good agreement, but also revealed some differences due to the fact that IAP is particularly well suited to classification problems. (C) 1998 Elsevier Science B.V. All rights reserved.