Novelty detection with constructive probabilistic neural networks

Abstract

This paper investigates the use of probabilistic neural networks trained with the dynamic decay adjustment algorithm (PNN–DDA) for novelty detection tasks. PNN–DDA is a fast, constructive neural model originally developed and investigated for standard classification tasks. The training algorithm is controlled by two parameters, θ + and θ - . Simulations employing four data sets from the UCI machine learning repository are reported. The results show that parameter θ - considerably influences the performance of PNN–DDA for novelty detection, and furthermore, that PNN–DDA achieves performance comparable to NNDD with the advantage of producing much smaller classifiers.