Systematic Literature Review of Mixed Variables Classification
Keywords:
Classification; Location Model; Smoothed Location Model; Mixed Variables; Systematic Literature Review.Abstract
Classification has become a widely used methodology across various fields. Numerous techniques are available for categorizing objects into distinct classes. Among these, the parametric approach, specifically the location model and its smoothed counterpart, has gained significant attention, particularly for data containing a mix of continuous and categorical variables. This paper systematically reviews literature from Scopus and Science Direct databases, focusing on a parametric classification approach involving mixed variables, i.e., location model and smoothed location model. A total of 70 articles were selected through systematic review procedures aligned with relevant thematic areas. These articles were analysed and discussed in the context of mixed classification based on the location model and smoothed location model across diverse data scenarios. Systematic literature reviews offer several advantages over traditional methods, notably providing a structured review process and essential priorities to mitigate research biases effectively. The review reveals that the smoothed location model demonstrates superior classification performance compared to other techniques in most studies. However, literature specifically addressing the applications of the location model and smoothed location model for classifying objects with mixed variables remains limited. Consequently, it is recommended that future research endeavours consider both models when dealing with classification tasks involving mixtures of variables.
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