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Proaftn is a fuzzy classification method that belongs to the class of supervised learning algorithms. The acronym Proaftn stands for: (PROcédure d'Affectation Floue pour la problématique du Tri Nominal), which means in English: Fuzzy Assignment Procedure for Nominal Sorting.
The method enables to determine the fuzzy indifference relations by generalizing the indices (concordance and discordance) used in the ELECTRE III method.[1] To determine the fuzzy indifference relations, PROAFTN uses the general scheme of the discretization technique described in,[2] that establishes a set of pre-classified cases called a training set.
To resolve the classification problems, Proaftn proceeds by the following stages:[3]
Stage 1. Modeling of classes: In this stage, the prototypes of the classes are conceived using the two following steps:
Direct technique: It consists in adjusting the parameters through the training set and with the expert intervention.
Indirect technique: It consists in fitting the parameters without the expert intervention as used in machine learning approaches.[4][5]
In multicriteria classification problem, the indirect technique is known as preference disaggregation analysis.[6] This technique requires less cognitive effort than the former technique; it uses an automatic method to determine the optimal parameters, which minimize the classification errors.
Furthermore, several heuristics and metaheuristics were used to learn the multicriteria classification method Proaftn.[7][8]
Stage 2. Assignment: After conceiving the prototypes, Proaftn proceeds to assign the new objects to specific classes.
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