Abstract
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.
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Ravi, V., De, R. (2015). Principal Component Analysis and General Regression Auto Associative Neural Network Hybrid as One-Class Classifier. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_15
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DOI: https://doi.org/10.1007/978-3-319-20294-5_15
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