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Principal Component Analysis and General Regression Auto Associative Neural Network Hybrid as One-Class Classifier

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8947))

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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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Author information

Authors and Affiliations

  1. Center of Excellence in CRM and Analytics, Institute for Development and Research in Banking Technology, Castle Hills Road #1, Masab Tank, Hyderabad, 500057, Andhra Pradesh, India

    Vadlamani Ravi

  2. Departments of Mathematics and Statistics, IIT Kanpur, Kanpur, 208016, Uttar Pradesh, India

    Ranabir De

Authors
  1. Vadlamani Ravi
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  2. Ranabir De
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Correspondence to Vadlamani Ravi .

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Editors and Affiliations

  1. Department of Electrical Engineering, IIT, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

    Swagatam Das

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20293-8

  • Online ISBN: 978-3-319-20294-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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