Prototype Selection for Composite Nearest Neighbor Classifiers

PROTOTYPE SELECTION FOR COMPOSITE NEAREST NEIGHBOR CLASSIFIERS July 1995 DAVID B. SKALAK, B.S., Union College M.A., Dartmouth College J.D., Harvard Law School M.S., University of Massachusetts Amherst Directed by: Professor Edwina L. Rissland This proposal brings together two problems in classification. The first problem is how to design one of the simplest and oldest classifiers, the k-nearest neighbor classifier. The second problem is how to combine classifiers to produce a more effective classifier. Our immediate objective is to study a classifier that combines the predictions of a set of complementary nearest neighbor classifiers using several well-known machine learning algorithms. We use the term complementary to refer to a set of classifiers whose predictions may be combined to yield a classifier with accuracy higher than any of these component classifiers. The resulting architecture is an instantiation of the stacked generalization framework discussed by Wolpert [ 1992 ] ...