Proaftn
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:
- 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.[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.
References
- ↑ Roy, B. (1996). Multicriteria Methodology for Decision Aiding. Dordrecht: Kluwer Academic.
- ↑ Ching, J.Y. (1995). "Class-dependent discretization for inductive learning from continuous and mixed-mode data". IEEE Transactions on Pattern Analysis and Machine Intelligence 17 (7): 641–651. doi:10.1109/34.391407.
- ↑ Belacel, N. (2000). "Multicriteria assignment method PROAFTN: Methodology and medical application". European Journal of Operational Research 125 (3): 175–83. doi:10.1016/s0377-2217(99)00192-7.
- ↑ Doumpos, M.; Zopounidis, C. (2011). "Preference disaggregation and statistical learning for multicriteria decision support: A review". European Journal of Operational Research 209 (3): 203–214. doi:10.1016/j.ejor.2010.05.029.
- ↑ Belacel, N.; Rava, H. B.l; Punnen, A. P. (2007). "Learning multicriteria fuzzy classification method PROAFTN from data". Computers & Operations Research 34 (7): 1885–1898. doi:10.1016/j.cor.2005.07.019. https://nrc-publications.canada.ca/eng/view/accepted/?id=ce2dc2ef-277f-40ee-a2d5-13d4aa4211bc.
- ↑ Jacquet-Lagrèze, E.; Siskos, J. (2001). "Preference disaggregation: Twenty years of MCDA experience". European Journal of Operational Research 130 (2): 233–245. doi:10.1016/s0377-2217(00)00035-7.
- ↑ Al-Obeidat, F. (2011). "An evolutionary framework using particle swarm optimization for classification method PROAFTN". Applied Soft Computing 11 (8): 4971–4980. doi:10.1016/j.asoc.2011.06.003. https://nrc-publications.canada.ca/eng/view/accepted/?id=ab0d4beb-dfd6-4ec8-97e3-2f052515ccdd.
- ↑ Al-Obeidat, f. (2010). "Differential Evolution for learning the classification method PROAFTN". Knowledge-Based Systems 23 (5): 418–426. doi:10.1016/j.knosys.2010.02.003.
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