Novelty detection with constructive probabilistic neural networks

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+θ+ and θ-θ-. Simulations employing four data sets from the UCI machine learning repository are reported. The results show that parameter θ-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.