Variable extractions using principal component analysis and multiple correspondence analysis for large number of mixed variables classification problems

Non-parametric smoothed location model is another powerful approach which can be used to discriminate the objects that contain both continuous and binary variables.However, the smoothed location model is infeasible in estimating parameters when a large number of binary variables involved in the study.To handle this issue, the combination of two variable extraction techniques namely principal component analysis (PCA) and multiple correspondence analysis (MCA) are carried out before the construction of the smoothed location model. In fact, there are four types of MCA but only Indicator MCA and joint correspondence analysis (JCA) will be discussed in this article.Thus, the performance of the smoothed location model together with combination of PCA and two types of MCA, i.e. Indicator MCA and JCA, will be compared and evaluated.The overall results from simulation study show that the smoothed location model performed better when the binary extraction is done by JCA rather than the Indicator MCA in terms of misclassification rate and computational efficiency