Proaftn

From HandWiki

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

  1. Roy, B. (1996). Multicriteria Methodology for Decision Aiding. Dordrecht: Kluwer Academic. 
  2. 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. 
  3. 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. 
  4. 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. 
  5. 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. 
  6. 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. 
  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. 
  8. 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. 

External links