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.

Additional Metadata
Keywords Hybrid-type Prototype Reduction, Nonstatinoary Environments, Prototype Reduction Schemes (PRS), Time Varying Samples (TVS)
Persistent URL dx.doi.org/10.1007/11589990_64
Series Lecture Notes in Computer Science
Citation
Kim, S.-W. (Sang-Woon), & Oommen, J. (2005). Time-varying prototype reduction schemes applicable for non-stationary data sets. In Lecture Notes in Computer Science. doi:10.1007/11589990_64