THE IMPACT OF MEASUREMENT SCALE AND CORRELATION STRUCTURE ON CLASSIFICATION PERFORMANCE OF INDUCTIVE LEARNING AND STATISTICAL-METHODS

This is a comparative study of inductive learning and statistical meth ods using the simulation approach to provide a generalizable results. The purpose of this study is to investigate the impact of measurement scale of explanatory variables on the relative performance of the stat istical method (probit) and the inductive learning method (ID3) and to examine the impact of correlation structure on the classification beh avior of the probit method and the ID3 method. The simulation results show that the relative classification accuracy of ID3 to probit increa ses as the proportion of binary variables increases in the classificat ion model, and that the relative accuracy of lD3 to probit is higher w hen the covariance matrices are unequal among populations than when th e covariance matrices are equal among populations. The empirical tests on ID3 reveal that the classification accuracy of lD3 is lower when t he covariance matrices are unequal among populations than when the cov ariance matrices are equal among populations and that the classificati on accuracy of lD3 decreases as the correlations among explanatory var iables increases.