Algorithm Selection for Machine Learning Classification: An Application of the MELCHIOR Multicriteria Method

Abstract

This paper aims to select an algorithm for the Machine Learning (ML) classification task. For the proposed analysis, the Multi-criteria Decision Aid (MCDA) Méthode d’ELimination et de CHoix Includent les relations d’ORdre (MELCHIOR) method was applied. The experiment considered the following criteria as relevant: Accuracy, sensitivity, and processing time of the algorithms. The data used refers to the intention of buying on the Internet and the purpose is to predict whether the customer will finalize a particular purchase. Among various MCDA techniques available, MELCHIOR was chosen to support the decision-making process because this method provides the evaluation of alternatives without the need to elicit the weights of the criteria. As a result, the Gradient Boosting Decision Tree algorithm has been selected as the most suitable for the ML classification task.

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