Data reduction using classifier ensembles

We propose a data reduction approach for finding a reference set for the nearest neighbour classifier. The approach is based on classifier ensembles. Each ensemble member is given a subset of the training data. Using Wilson’s editing method, the ensemble member produces a reduced reference set. We explored several routes to make use of these reference sets. The results with 10 real and artificial data sets indicated that merging the reference sets and subsequent editing of the merged set provides the best trade-off between the error and the size of the resultant reference set. This approach can also handle large data sets because only small fractions of the data are edited at a time.