In this paper we develop the principal component analysis (PCA) and general regression auto associative neural network (GRAANN) based hybrid as a one-class classifier (PCA-GRAANN). We test the effectiveness of PCA-GRAANN on bankruptcy prediction datasets namely Spanish banks, Turkish banks, US banks and UK banks; UK credit dataset and the benchmark WBC dataset. When compared the results of another recently proposed hybrid, particle swarm optimization trained auto associative neural network (PSOAANN) [1], PCA-GRAANN yielded mixed results. We conclude that PCA-GRAANN can be used as a viable alternative for any one-class classifier.
CITATION STYLE
Ravi, V., & De, R. (2015). Principal component analysis and general regression auto associative neural network hybrid as one-class classifier. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8947, pp. 164–175). Springer Verlag. https://doi.org/10.1007/978-3-319-20294-5_15
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