Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
In this paper, we propose a novel approach to distance measurement for rankings, introducing a new metric that exhibits exceptional properties. Our proposed distance metric is defined within the interval of 0 to 1, ensuring a compact and standardized representation. Importantly, we demonstrate that this distance metric satisfies all the essential criteria to be classified as a true metric. By adhering to properties such as non-negativity, identity of indiscernibles, symmetry, and the crucial triangle inequality, our proposed distance metric provides a robust and reliable approach for comparing rankings in a rigorous and mathematically sound manner. Finally, we compare our new metric with distances such as Hamming distance, Canberra distance, Bray-Curtis distance, Euclidean distance, Manhattan distance, and Chebyshev distance. By conducting simple experiments, we assess the performance and advantages of our proposed metric in comparison to these established distance measures. Through these comparisons, we demonstrate the superior properties and capabilities of our new drastic weighted similarity distance for accurately capturing the dissimilarities and similarities between rankings in the decision-making domain.
Rocznik
Tom
Strony
731--738
Opis fizyczny
Bibliogr. 25 poz., wz., tab., wykr.
Twórcy
autor
- West Pomeranian University of Technology in Szczecin ul. Żołnierska 49, 71-210 Szczecin, Poland
autor
- National Telecommunications Institute ul. Szachowa 1, 04-894 Warsaw, Poland
Bibliografia
- 1. S.-S. Choi, S.-H. Cha, C. C. Tappert, et al., “A survey of binary similarity and distance measures,” Journal of systemics, cybernetics and informatics, vol. 8, no. 1, pp. 43–48, 2010.
- 2. E. Deza, M. M. Deza, M. M. Deza, and E. Deza, Encyclopedia of distances. Springer, 2009.
- 3. S. Chen, B. Ma, and K. Zhang, “On the similarity metric and the distance metric,” Theoretical Computer Science, vol. 410, no. 24-25, pp. 2365–2376, 2009.
- 4. M. Zhu, V. Lakshmanan, P. Zhang, Y. Hong, K. Cheng, and S. Chen, “Spatial verification using a true metric,” Atmospheric research, vol. 102, no. 4, pp. 408–419, 2011.
- 5. L. Liberti, C. Lavor, N. Maculan, and A. Mucherino, “Euclidean distance geometry and applications,” SIAM review, vol. 56, no. 1, pp. 3–69, 2014.
- 6. S. Pandit, S. Gupta, et al., “A comparative study on distance measuring approaches for clustering,” International journal of research in computer science, vol. 2, no. 1, pp. 29–31, 2011.
- 7. R. Coghetto, “Chebyshev distance,” Formalized Mathematics, vol. 24, no. 2, pp. 121–141, 2016.
- 8. M. M. Deza, E. Deza, M. M. Deza, and E. Deza, “Distances and similarities in data analysis,” Encyclopedia of distances, pp. 291–305, 2013.
- 9. A. Bączkiewicz, J. Wątróbski, and W. Sałabun, “Distance metrics library for mcda methods,” in R. A. Buchmann, G. C. Silaghi, D. Bufnea, V. Niculescu, G. Czibula, C. Barry, M. Lang, H. Linger, C. Schneider (Eds.), Information Systems Development: Artificial Intelligence for Information Systems Development and Operations (ISD2022 Proceedings). Cluj-Napoca, Romania: Babes , -Bolyai University., pp. 1–8, 2022.
- 10. P. Zhang, X. Wang, and P. X.-K. Song, “Clustering categorical data based on distance vectors,” Journal of the American Statistical Association, vol. 101, no. 473, pp. 355–367, 2006.
- 11. H. A. Abu Alfeilat, A. B. Hassanat, O. Lasassmeh, A. S. Tarawneh, M. B. Alhasanat, H. S. Eyal Salman, and V. S. Prasath, “Effects of distance measure choice on k-nearest neighbor classifier performance: a review,” Big data, vol. 7, no. 4, pp. 221–248, 2019.
- 12. G. Glazko, A. Gordon, and A. Mushegian, “The choice of optimal distance measure in genome-wide datasets,” Bioinformatics, vol. 21, no. Suppl_3, pp. iii3–iii11, 2005.
- 13. R. Kumar and S. Vassilvitskii, “Generalized distances between rankings,” in Proceedings of the 19th international conference on World wide web, pp. 571–580, 2010.
- 14. W. Sałabun, J. Wątróbski, and A. Shekhovtsov, “Are mcda methods benchmarkable? a comparative study of topsis, vikor, copras, and promethee ii methods,” Symmetry, vol. 12, no. 9, p. 1549, 2020.
- 15. A. Karczmarczyk, J. Wątróbski, G. Ladorucki, and J. Jankowski, “Mcda-based approach to sustainable supplier selection,” in 2018 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 769–778, IEEE, 2018.
- 16. D. K. Bukovšek and B. Mojškerc, “On the exact region determined by spearman’s footrule and gini’s gamma,” Journal of Computational and Applied Mathematics, vol. 410, p. 114212, 2022.
- 17. W. Sałabun and K. Urbaniak, “A new coefficient of rankings similarity in decision-making problems,” in Computational Science–ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3–5, 2020, Proceedings, Part II 20, pp. 632–645, Springer, 2020.
- 18. C. Genest and J.-F. Plante, “On blest’s measure of rank correlation,” Canadian Journal of Statistics, vol. 31, no. 1, pp. 35–52, 2003.
- 19. A. Shekhovtsov, V. Kozlov, V. Nosov, and W. Sałabun, “Efficiency of methods for determining the relevance of criteria in sustainable transport problems: A comparative case study,” Sustainability, vol. 12, no. 19, p. 7915, 2020.
- 20. W. Sałabun, A. Shekhovtsov, D. Pamučar, J. Wątróbski, B. Kizielewicz, J. Więckowski, D. Bozanić, K. Urbaniak, and B. Nyczaj, “A fuzzy inference system for players evaluation in multi-player sports: The football study case,” Symmetry, vol. 12, no. 12, p. 2029, 2020.
- 21. B. Bera, P. K. Shit, N. Sengupta, S. Saha, and S. Bhattacharjee, “Susceptibility of deforestation hotspots in terai-dooars belt of himalayan foothills: A comparative analysis of vikor and topsis models,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 10, pp. 8794–8806, 2022.
- 22. M. Norouzi, D. J. Fleet, and R. R. Salakhutdinov, “Hamming distance metric learning,” Advances in neural information processing systems, vol. 25, 2012.
- 23. G. Jurman, S. Riccadonna, R. Visintainer, and C. Furlanello, “Canberra distance on ranked lists,” in Proceedings of advances in ranking NIPS 09 workshop, pp. 22–27, Citeseer, 2009.
- 24. E. W. Beals, “Bray-curtis ordination: an effective strategy for analysis of multivariate ecological data,” in Advances in ecological research, vol. 14, pp. 1–55, Elsevier, 1984.
- 25. M. Malkauthekar, “Analysis of euclidean distance and manhattan distance measure in face recognition,” in Third International Conference on Computational Intelligence and Information Technology (CIIT 2013), pp. 503–507, IET, 2013.
Uwagi
1. The work was supported by the National Science Centre 2021/41/B/HS4/01296 (W.S. and A.S.).
2. Thematic Tracks Regular Papers
3. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bac3f740-946e-4a52-bf8d-5cbd9b166ba3