Reducing Training Sets by NCN-based Exploratory Procedures
In this paper, a new approach to training set size reduction is presented. This scheme basically consists of defining a small number of prototypes that represent all the original instances. Although the ultimate aim of the algorithm proposed here is to obtain a strongly reduced training set, the performance is empirically evaluated over nine real datasets by comparing not only the reduction rate but also the classification accuracy with those of other condensing techniques.
KeywordsNear Neighbour Pattern Recognition Letter Prototype Selection Prototype Selection Method Lower Reduction Percentage
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