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Optimizing a Neural Tree Using Subtree Retraining

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3215))

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Abstract

Subtree retraining applied to a Stochastic Competitive Evolutionary Neural Tree model (SCENT) is introduced. This subtree retraining process is designed to improve the performance of the original model which provides a hierarchical classification of unlabelled data. The effect of subtree retraining on the network produces stable classificatory structures by repeatedly restructuring the weakest branch of the classification tree based on internal relation between members. An experimental comparison using well-known real world data sets, chosen to provide a variety of clustering scenarios, showed the new approach produced more reliable performances.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Khon Kaen University, 40002, Thailand

    Wanida Pensuwon

  2. Department of Computer Science, Faculty of Engineering and Information Science, University of Hertfordshire, Hatfield, Herts, AL10 9AB, United Kingdom

    Wanida Pensuwon, Rod Adams & Neil Davey

Authors
  1. Wanida Pensuwon
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  2. Rod Adams
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  3. Neil Davey
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Editor information

Editors and Affiliations

  1. KES International, 2nd Floor, 145-157 St John Street, EC1V 4PY, London, United Kingdom

    Mircea Gh. Negoita

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, SA, Australia

    Lakhmi C. Jain

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© 2004 Springer-Verlag Berlin Heidelberg

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Pensuwon, W., Adams, R., Davey, N. (2004). Optimizing a Neural Tree Using Subtree Retraining. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30134-9_35

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  • DOI: https://doi.org/10.1007/978-3-540-30134-9_35

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23205-6

  • Online ISBN: 978-3-540-30134-9

  • eBook Packages: Springer Book Archive

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