Particle Swarm Optimization Trained Auto Associative Neural Networks Used as Single Class Classifier

Vadlamani, R and Nekuri, N and Das, M (2012) Particle Swarm Optimization Trained Auto Associative Neural Networks Used as Single Class Classifier. In: Swarm, Evolutionary, and Memetic Computing: Third International Conference, SEMCCO 2012, Bhubaneswar, India, December 20-22, 2012. Proceedings. Lecture Notes in Computer Science (7677). Springer Berlin Heidelberg, pp. 577-584. ISBN 978-3-642-35380-2

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Abstract

We propose the particle swarm optimization (PSO) trained auto associative neural network (AANN) as a single class classifier (PSOAANN). The proposed architecture consists of three layers namely input layer, hidden layer and output layer unlike that of the traditional AANN. The efficacy of the proposed single class classifier is evaluated on bankruptcy prediction datasets namely Spanish banks, Turkish banks, US banks and UK banks; UK credit dataset and the benchmark WBC dataset. PSOAANN achieved better results when compared to Modified Great Deluge Algorithm trained auto associative neural network (MGDAAANN) [1]. It is concluded that PSOAANN as a single class classifier can be used as an effective tool in classifying datasets, where the class of interest (usually the positive class) is either totally missing or disproportionately present in the training data, which is the case in many real life problems for e.g. financial fraud detection.

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