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Time-Varying Prototype Reduction Schemes Applicable for Non-stationary Data Sets

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 3809))

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Abstract

All of the Prototype Reduction Schemes (PRS) which have been reported in the literature, processtime-invariant data to yield a subset of prototypes that are useful in nearest-neighbor-like classification. In this paper, we suggest two time-varying PRS mechanisms which, in turn, are suitable for twodistinct models of non-stationarity. In both of these models, rather than process all the data as a whole set using a PRS, we propose that the information gleaned from a previous PRS computation be enhanced to yield the prototypes for the current data set, and this enhancement is accomplished using a LVQ3-type “fine tuning”. The experimental results, which to our knowledge are the first reported results applicable for PRS schemes suitable for non-stationary data, are, in our opinion, very impressive.

The first author was partially supported by KOSEF, the Korea Science and Engineering Foundation, and the second author was partially supported by NSERC, Natural Sciences and Engineering Research Council of Canada.

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Author information

Authors and Affiliations

  1. IEEE, Dept. of Computer Science and Engineering, Myongji University, Yongin, 449-728, Korea

    Sang-Woon Kim

  2. IEEE, School of Computer Science, Carleton University, Ottawa, ON, K1S 5B6, Canada

    B. John Oommen

Authors
  1. Sang-Woon Kim

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  2. B. John Oommen

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Editor information

Editors and Affiliations

  1. Guangxi Normal University, College of CS and IT, Guilin, China, and University of Technology, Faculty of Engineering and Information Technology, Sydney, Australia

    Shichao Zhang

  2. Department of Electrical and Computer Systems Engineering, Monash University, 3800, Melbourne, Victoria, Australia

    Ray Jarvis

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© 2005 Springer-Verlag Berlin Heidelberg

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Kim, SW., Oommen, B.J. (2005). Time-Varying Prototype Reduction Schemes Applicable for Non-stationary Data Sets. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_64

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