A NONPARAMETRIC PARTITIONING PROCEDURE FOR PATTERN CLASSIFICATION

A nonparametric procedure is developed for determining a structure for multivariate, multiclass pattern classification. The resultant classifier is in the form of a layered machine which is composed of multithreshold elements. The basic algorithm determines partitions which are parallel hyperplanes orthogonal to the feature coordinate dimensions. Inherent in the procedure is the concept of a transgenerator unit used to establish new feature dimensions such that effective partitioning can be obtained. While the choice of which classes of transgeneration units to consider is ultimately up to the user, a number of such units are suggested herein. The algorithm gives an indication as to the effectiveness of various transgeneration units and hence can also be used in an interactive manner if so desired for the actual design of a classification structure. A method for placing a confidence bound on the probability of error associated with the classifier is presented; this bound is based on the concept of nonparametric tolerance regions and, as a result, does not require a testing sample set. Examples involving real data in agricultural remote sensing are given to illustrate the application of the procedure. Copyright © 1969 by The Institute of Electrical and Electronics Engineers, Inc.