This research’s primary goal was to evaluate the performance analysis of the recently constructed smoothed location models (SLMs) for discrimination purposes by combining two kinds of multiple correspondence analysis (MCA) to handle high dimensionality problems arising from the binary variables. A previous study of SLM, together with MCA as well as principal component analysis (PCA), displayed that the misclassification rate was still very high with respect to a large number of binary variables. Thus, two new SLMs are constructed in this paper to solve this particular problem. The first model results from the combination of SLM with Burt MCA (denoted as SLM+Burt), and the second one is with the joint correspondence analysis (denoted as SLM+JCA). The findings showed that both models performed well for all sample sizes (n) and all binary variables (b) under investigation, except n=60 and b=25 for the SLM+JCA model. Overall, the SLM+JCA model yields a greater performance in contrast to the SLM+Burt model. Moreover, the concept and procedures of the discrimination for the two-group classification conducted in this paper can be extended to multi-class classification as practitioners often deal with many groups and complexities of variables.
