A framework of mixed variables classification in the presence of outliers: A robust location model

Abstract

The natural outstanding of location model is as an excellent tool for mixed variables classification among other existing approaches such as Kernel-based non-parametric classification, logistic discrimination and linear discriminant analysis. However, the presence of outliers will affect the estimation of population parameters, hence causing inability of the model to provide an adequate statistical model and interpretation as well. In other words, outliers can distort not only parameters estimation, but also lead to poor classification performance. Therefore, this article aims to develop a new framework of location model through the integration of robust technique and classical location model with mixed variables in the presence of outliers, purposely for robust classification. The developed framework produces a new location model that can be used as an alternative approach for classification tasks as for academicians and practitioners in future applications, especially when they are facing with outliers in the data samples. We hope that the new location model produced will have better performance for both contaminated and non-contaminated datasets.