A Novel Weighted Preference Relation Approach to Detect Outliers in Multi-Criteria Decision Aid Context

• Autorzy: Achir Toufik , Rouba Baroudi

Identyfikatory

DOI

10.2478/fcds-2025-0005

• Języki publikacji: EN

Abstrakty

EN

The problem of outlier detection in the multicriteria decision aid (MCDA) field has not been extensively explored in the current literature. This study presents a novel approach to tackle this challenge, based on two key concepts. Firstly, the degree of importance of a preference relation, which utilizes multicriteria preference indices (derived from the PROMETHEE method) to assess the significance level of a preference relation. Secondly, the similarity of alternatives, which uses the degree of importance to evaluate how similar each alternative is to the others. Based on the distribution of these similarities, outliers are identified using either the Interquartile Range (IQR) method or the Standard Deviations (SD) method. The proposed approach is applied to two real-world scenarios: the world happiness ranking problem and the human development index problem.

Słowa kluczowe

EN

degree of importance; preference relation; multicriteria decision aid; PROMETHEE; outlier detection; statistical methods

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2025
• Tom: Vol. 50, No. 2
• Strony: 117--156
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Achir Toufik

• Lab CSTL, Mostaganem University, 27000 Mostaganem, Algeria
• Faculty of Exact Sciences and Computer Science, Mostaganem University

autor

Rouba Baroudi

• Lab CSTL, Mostaganem University, 27000 Mostaganem, Algeria
• Faculty of Science and technology, Mostaganem University

Bibliografia

• [1] Barnett V., Lewis T. Outliers in statistical data John Wiley and Sons. New York 1994.
• [2] Bland J. D. G. (1996) ‘Statistics notes: measurement error.’ BMJ n.d.; 312:1654.
• [3] Bolton R. J., Hand D. J. Unsupervised profiling methods for fraud detection. Credit Scoring Credit Control VII 2001: 235-55.
• [4] Brans J.-P., Mareschal B. The PROMCALC & GAIA decision support system for multicriteria decision aid. Decis Support Syst 1994; 12: 297-310. https://doi.org/https://doi.org/10.1016/0167-9236(94)90048-5.
• [5] Brans J-P, Vincke P, Mareschal B. How to select and how to rank projects: The PROMETHEE method. Eur J Oper Res 1986; 24: 228-38.
• [6] Breunig M. M., Kriegel H.-P., Ng R. T., Sander J. LOF: identifying density-based local outliers. Proc. 2000 ACM SIGMOD Int. Conf. Manag. data, 2000, p. 93-104.
• [7] Dang T. T., Ngan H. Y. T., Liu W. Distance-based k-nearest neighbors outlier detection method in large-scale traffic data. 2015 IEEE Int. Conf. Digit. Signal Process., IEEE; 2015, p. 507-10.
• [8] Dejaegere G., De Smet Y. Promethee γ: A new Promethee based method for partial ranking based on valued coalitions of monocriterion net flow scores. J Multi-Criteria Decis Anal 2023; 30: 147-60.
• [9] Dekking F. M., Kraaikamp C., Lopuhaä H. P., Meester L. E. A Modern Introduction to Probability and Statistics: Understanding why and how. vol. 488. Springer; 2005.
• [10] De Smet Y., Guzmán L. M. Towards multicriteria clustering: An extension of the k-means algorithm. Eur J Oper Res 2004; 158: 390-8. https://doi.org/10.1016/j.ejor.2003.06.012.
• [11] De Smet Y., Hubinont J.-P. P., Rosenfeld J. A note on the detection of outliers in a binary outranking relation. Lect Notes Comput Sci (Including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 2017; 10173 LNCS: 151-9. https://doi.org/10.1007/978-3-319-54157-0_11.
• [12] De Smet Y., Nemery P., Selvaraj R. An exact algorithm for the multicriteria ordered clustering problem. Omega 2012; 40: 861-9.
• [13] Ding Q., Katenka N., Barford P., Kolaczyk E., Crovella M. Intrusion as (anti) social communication: characterization and detection. Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discov. data Min., 2012, p. 886-94.
• [14] Ishizaka A., Nemery P. Multi-attribute utility theory. Multi-Criteria Decis Anal Methods Softw 2013: 81-113.
• [15] Korhonen P. Interactive methods. Mult Criteria Decis Anal State Art Surv 2005: 641-61.
• [16] MacQueen J. Some methods for classification and analysis of multivariate observations. Proc. fifth Berkeley Symp. Math. Stat. Probab., vol. 1, Oakland, CA, USA; 1967, p. 281-97.
• [17] Mareschal B., Brans J.-P. Geometrical representations for MCDA. Eur J Oper Res 1988; 34: 69-77. https://doi.org/https://doi.org/10.1016/0377-2217(88)90456-0.
• [18] Moonesinghe H. D. K., Tan P-N. Outrank: a graph-based outlier detection framework using random walk. Int J Artif Intell Tools 2008; 17: 19-36.
• [19] Nguyen H. V., Ang H. H., Gopalkrishnan V. Mining outliers with ensemble of heterogeneous detectors on random subspaces. Database Syst. Adv. Appl. 15th Int. Conf. DASFAA 2010, Tsukuba, Japan, April 1-4, 2010, Proceedings, Part I 15, Springer; 2010, p. 368-83.
• [20] Pimentel T., Monteiro M., Veloso A., Ziviani N. Deep active learning for anomaly detection. 2020 Int. Jt. Conf. Neural Networks, IEEE; 2020, p. 1-8.
• [21] Rouba B. A net-flow based approach to detect outliers in multicriteria decision problems. Intell Decis Technol 2021; 15: 239-50. https://doi.org/10.3233/IDT-200046.
• [22] Rouba B., Nait-Bahloul S. Towards identifying multicriteria outliers: An outranking relation-based approach. Int J Decis Support Syst Technol 2018; 10: 27-38. https://doi.org/10.4018/IJDSST.2018070102.
• [23] Roy B. Multicriteria methodology for decision aiding. vol. 12. Springer Science & Business Media; 2013.
• [24] Roy B. Multicriteria methodology for decision aiding. vol. 12. Springer Science & Business Media; 1996.
• [25] Shapiro S. S., Wilk M. B. An analysis of variance test for normality (complete samples). Biometrika 1965; 52: 591-611.
• [26] Tsou Y.-L., Chu H.-M., Li C., Yang S.-W. Robust distributed anomaly detection using optimal weighted one-class random forests. 2018 IEEE Int. Conf. Data Min., IEEE; 2018, p. 1272-7.
• [27] Vincke P. Multicriteria decision-aid. John Wiley & Sons; 1992.
• [28] World Happiness Report 2015. https://www.kaggle.com/datasets/mathurinache/world-happiness-report?resource=download&select=2015.csv.
• [29] Yang X., Latecki L. J., Pokrajac D. Outlier detection with globally optimal exemplar-based GMM. Proc. 2009 SIAM Int. Conf. data Min., SIAM; 2009, p. 145-54.