Comparison of various procedures for estimation of the classification error in discriminance analysis

The practical use of classification methods depends, for the majority of cases, on whether it is possible to obtain evidence how newly appearing objects of unknown classes-membership can be allocated on the basis of a classifier established by a training sample. In the present paper several error estimation methods are presented and compared with each other. It is shown by means of a practical example in which way the number of characteristics used in the discrimination affects the classification error. Discriminance functions calculated from many characteristics yield a higher error rate than those from fewer characteristics. From the comparison of the error rates and computing times involved in the individual methods recommendations for the selection of practicable estimation methods are given.