User:Nbelacel/PROAFTN

PROAFTN is a 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 Assignmemt Procedure for Nominal Sorting. The method was first proposed by Nabil Belacel in 1999 in his PhD thesis since that PROAFTN methodology was published in several papers and proceedings. The first paper presented the general description of PROAFTN methodology was published in the Europen Journal of Operational Research. To resolve the classification problems, PROAFTN proceeds by the following stages: Stage 1. Modeling of classes: In this stage, the prototypes of the classes are conceived using the two following steps: • Step 1. Structuring: The prototypes and their parameters (thresholds, weights, etc.) are established using the available knowledge given by the expert. • Step 2. Validation: We use one of the two following techniques in order to validate or adjust the parameters obtained in the first step through the assignment examples known as a training set. • 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. . 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. Stage 2: Assignment: After conceiving the prototypes, we proceed to assign the new objects to specific classes.