Time-varying prototype reduction schemes applicable for non-stationary data sets

Authors:
Sang-Woon Kim;B. John Oommen
Affiliations:
IEEE, Dept. of Computer Science and Engineering, Myongji University, Yongin, Korea;IEEE, School of Computer Science, Carleton University, Ottawa, ON, Canada
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

All of the Prototype Reduction Schemes (PRS) which have been reported in the literature, process time-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 two distinct 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.