Multiple correspondence analysis for handling large binary variables in smoothed location model - Ngu, Penny Ai Huong; Hamid, Hashibah; Aziz, Nazrina (IP Publishing LLC, 2015)
Smoothed location model is a discriminant analysis which can be used to handle the data involving mixtures of continuous and binary variables simultaneously.This model is introduced to handle the problem of some empty cells due to the increasing of binary variables.However, smoothed location model is infeasible if involve large number of binary variables.Therefore, the combination of two variable extraction approaches, principal component analysis and multiple correspondence analysis are carried out before the construction of smoothed location model in order to extract large number of measured variables in the study.In fact, there are four types of multiple correspondence analysis but only Burt matrix multiple correspondence analysis had been applied in the latest investigation. Thus, this study aims to examine and compare principal component analysis with four types of multiple correspondence analysis and hope to have better results for data with large number of mixed variables.The proposed model is expected to provide a better or at least comparable classification performance as comparing to others classification methods.