Distance-based classifiers are effective at discriminating between populations that differ in location. But scale difference can mask location differences and such classifiers may perform poorly if the information for classification accumulates through a large number of relatively small location differences in data components. For some classifiers such as those based on the support vector machine or the centroid method, scale corrections are important primarily in the case of small training-sample sizes. However, for other classifiers, including those based on nearest-neighbor and average distance methods, scale adjustments are helpful more generally. (20 refs.)
展开▼
