Some Classification Procedures for Multivariate Binary Data Using Orthogonal Functions

Abstract

Four new methods for classification of multivariate binary data are presented, based on an orthogonal expansion of the density in terms of discrete Fourier series. The performance of these methods in 11 populations of various structures was measured in terms of mean error of misclassification and was compared to three well-known methods. Also, performance in density estimation was measured for the appropriate methods. In general, the new methods seem to be superior for classification as well as for density estimation.