Small sample size effects in the use of editing techniques

Editing is recognized as a useful, convenient and even mandatory preprocessing to be carried out prior to using nearest-neighbor (NN) classification techniques. The performance of editing has only been shown for large sets of data but, in practice, one can seldom afford such large sets; either because of the cost of data collection and/or because of computational costs of the adopted editing technique. The authors present evidence showing that editing, and multiedit in particular, dramatically degrade the results as the size of the data set becomes smaller. In conclusion, a modification of the multiedit which can behave better under the small sample assumption is proposed