Pruning with interval arithmetic perceptron

Abstract

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.