Letters: Novelty detection with constructive probabilistic neural networks

Authors:
Adriano L. I. Oliveira;Flavio R. G. Costa;Clovis O. S. Filho
Affiliations:
Department of Computing Systems, Polytechnic School of Engineering, Pernambuco State University, Rua Benfica, 455, Madalena, 50.750-410 Recife-PE, Brazil;Department of Computing Systems, Polytechnic School of Engineering, Pernambuco State University, Rua Benfica, 455, Madalena, 50.750-410 Recife-PE, Brazil;Department of Computing Systems, Polytechnic School of Engineering, Pernambuco State University, Rua Benfica, 455, Madalena, 50.750-410 Recife-PE, Brazil
Venue:
Neurocomputing
Year:
2008

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