MLP-COMET-based decision model re-identification for continuous decision-making in the complex network environment

Kizielewicz, Bartłomiej; Więckowski, Jakub; Jankowski, Jarosław

doi:10.15439/2023F5438

Artykuł - szczegóły

Tytuł artykułu

MLP-COMET-based decision model re-identification for continuous decision-making in the complex network environment

Autorzy

Kizielewicz Bartłomiej , Więckowski Jakub , Jankowski Jarosław

Wybrane pełne teksty z tego czasopisma

http://annals-csis.org

Identyfikatory

DOI

10.15439/2023F5438

Warianty tytułu

Języki publikacji

Abstrakty

In recent years, complex networks have gained significant attention for their practical potential in data analysis and decision-making. However, assessing node relevance in complex networks poses challenges, including subjectivity and difficulty reproducing criteria relationships. To address these issues, we propose MLP-COMET. This novel approach combines the Multi-Layer Perceptron (MLP) with the Characteristic Objects Method (COMET) in Multi-Criteria Decision Analysis (MCDA). MLP-COMET aims to re-identify decision models using MLP to evaluate characteristic objects. We evaluate the approach to assessing the complex network and demonstrate its effectiveness in evaluating without heavy reliance on domain experts. The MLP-COMET performance is evaluated through ranking comparisons, showing a strong correlation with reference expert rankings. We also analyze the impact of training sample size and number of characteristic objects on ranking similarity, observing high stability and similarity using the $r\_w$ metric. MLP-COMET offers an effective and reliable tool for evaluating complex networks and facilitating decision-making processes.

Słowa kluczowe

measurement computer science data analysis correlation computer network reliability stability criteria

pomiar informatyka analiza danych korelacja niezawodność sieci kryteria stabilności

Wydawca

Polskie Towarzystwo Informatyczne

Czasopismo

Annals of Computer Science and Information Systems

Rocznik

2023

Tom

Vol. 35

Strony

591--602

Opis fizyczny

Bibliogr. 65 poz., tab., wz., wykr.

Twórcy

autor

Kizielewicz Bartłomiej

bartlomiej-kizielewicz@zut.edu.pl

Dept. of Artificial Intelligence Methods and Applied Mathematics, West Pomeranian University of Technology in Szczecin ul. Żołnierska 49, 71-210 Szczecin, Poland

https://orcid.org/0000-0001-5736-4014

autor

Więckowski Jakub

j.wieckowski@il-pib.pl

National Institute of Telecommunications ul. Szachowa 1, 04-894 Warsaw, Poland

https://orcid.org/0000-0002-9324-3241

autor

Jankowski Jarosław

jjankowski@zut.edu.pl

Dept. of Artificial Intelligence Methods and Applied Mathematics, West Pomeranian University of Technology in Szczecin ul. Żołnierska 49, 71-210 Szczecin, Poland

https://orcid.org/0000-0002-3658-3039

Bibliografia

1. T. C. Silva and L. Zhao, Machine learning in complex networks. Springer, 2016.
2. J. Biamonte, M. Faccin, and M. De Domenico, “Complex networks from classical to quantum,” Communications Physics, vol. 2, no. 1, p. 53, 2019.
3. C. W. Lynn, L. Papadopoulos, A. E. Kahn, and D. S. Bassett, “Human information processing in complex networks,” Nature Physics, vol. 16, no. 9, pp. 965–973, 2020.
4. B. Yang and J. Li, “Complex network analysis of three-way decision researches,” International Journal of Machine Learning and Cybernetics, vol. 11, pp. 973–987, 2020.
5. L. Qiu, J. Zhang, and X. Tian, “Ranking influential nodes in complex networks based on local and global structures,” Applied intelligence, vol. 51, pp. 4394–4407, 2021.
6. A. S. d. Mata, “Complex networks: a mini-review,” Brazilian Journal of Physics, vol. 50, pp. 658–672, 2020.
7. M. Zanin, D. Papo, P. A. Sousa, E. Menasalvas, A. Nicchi, E. Kubik, and S. Boccaletti, “Combining complex networks and data mining: why and how,” Physics Reports, vol. 635, pp. 1–44, 2016.
8. H. Xiong, M. Chen, C. Wu, Y. Zhao, and W. Yi, “Research on progress of blockchain consensus algorithm: a review on recent progress of blockchain consensus algorithms,” Future Internet, vol. 14, no. 2, p. 47, 2022.
9. S. Ferretti and G. D’Angelo, “On the ethereum blockchain structure: A complex networks theory perspective,” Concurrency and Computation: Practice and Experience, vol. 32, no. 12, p. e5493, 2020.
10. B. Tao, I. W.-H. Ho, and H.-N. Dai, “Complex network analysis of the bitcoin blockchain network,” in 2021 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–5, IEEE, 2021.
11. V. Hou Su, S. Sen Gupta, and A. Khan, “Automating ETL and Mining of Ethereum Blockchain Network,” in Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp. 1581– 1584, 2022.
12. L. Lü, D. Chen, X.-L. Ren, Q.-M. Zhang, Y.-C. Zhang, and T. Zhou, “Vital nodes identification in complex networks,” Physics reports, vol. 650, pp. 1–63, 2016.
13. H. Zhao, Z. Li, and R. Zhou, “Risk assessment method combining complex networks with MCDA for multi-facility risk chain and coupling in UUS,” Tunnelling and Underground Space Technology, vol. 119, p. 104242, 2022.
14. D. Pamucar, M. Yazdani, M. J. Montero-Simo, R. A. Araque-Padilla, and A. Mohammed, “Multi-criteria decision analysis towards robust service quality measurement,” Expert Systems with Applications, vol. 170, p. 114508, 2021.
15. R. Krishankumar and D. Pamucar, “Solving barrier ranking in clean energy adoption: An MCDM approach with q-rung orthopair fuzzy preferences,” International Journal of Knowledge-based and Intelligent Engineering Systems, no. Preprint, pp. 1–18, 2023.
16. M. Marttunen, J. Lienert, and V. Belton, “Structuring problems for Multi-Criteria Decision Analysis in practice: A literature review of method combinations,” European journal of operational research, vol. 263, no. 1, pp. 1–17, 2017.
17. M. Toslak, A. Ulutaş, S. Ürea, and Ž. Stević, “Selection of peanut butter machine by the integrated PSI-SV-MARCOS method,” International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 27, no. 1, pp. 73–86, 2023.
18. G. Odu, “Weighting methods for multi-criteria decision making technique,” Journal of Applied Sciences and Environmental Management, vol. 23, no. 8, pp. 1449–1457, 2019.
19. A. R. Paramanik, S. Sarkar, and B. Sarkar, “OSWMI: An objective-subjective weighted method for minimizing inconsistency in multi-criteria decision making,” Computers & Industrial Engineering, vol. 169, p. 108138, 2022.
20. Y. Liu, C. M. Eckert, and C. Earl, “A review of fuzzy AHP methods for decision-making with subjective judgements,” Expert Systems with Applications, vol. 161, p. 113738, 2020.
21. K. Rathi and S. Balamohan, “A mathematical model for subjective evaluation of alternatives in fuzzy multi-criteria group decision making using COPRAS method,” International Journal of Fuzzy Systems, vol. 19, pp. 1290–1299, 2017.
22. W. Ho and X. Ma, “The state-of-the-art integrations and applications of the analytic hierarchy process,” European Journal of Operational Research, vol. 267, no. 2, pp. 399–414, 2018.
23. J. Wi ̨eckowski, B. Kizielewicz, A. Shekhovtsov, and W. Sałabun, “RANCOM: A novel approach to identifying criteria relevance based on inaccuracy expert judgments,” Engineering Applications of Artificial Intelligence, vol. 122, p. 106114, 2023.
24. S. Kheybari, M. Kazemi, and J. Rezaei, “Bioethanol facility location selection using best-worst method,” Applied energy, vol. 242, pp. 612–623, 2019.
25. D. Pamučar, Ž. Stević, and S. Sremac, “A New Model for Determining Weight Coefficients of Criteria in MCDM Models: Full Consistency Method (FUCOM),” Symmetry, vol. 10, no. 9, p. 393, 2018.
26. M. R. Patel, M. P. Vashi, and B. V. Bhatt, “SMART-Multi-criteria decision-making technique for use in planning activities,” New Horizons in Civil Engineering (NHCE 2017), pp. 1–6, 2017.
27. K. Yang, N. Zhu, C. Chang, D. Wang, S. Yang, and S. Ma, “A methodological concept for phase change material selection based on multi-criteria decision making (MCDM): A case study,” Energy, vol. 165, pp. 1085–1096, 2018.
28. M. Shao, Z. Han, J. Sun, C. Xiao, S. Zhang, and Y. Zhao, “A review of multi-criteria decision making applications for renewable energy site selection,” Renewable Energy, vol. 157, pp. 377–403, 2020.
29. A. Karczmarczyk, J. Jankowski, and J. Wątróbski, “Multi-criteria decision support for planning and evaluation of performance of viral marketing campaigns in social networks,” PloS one, vol. 13, no. 12, p. e0209372, 2018.
30. F. M. Alexandrescu, L. Pizzol, A. Zabeo, E. Rizzo, E. Giubilato, and A. Critto, “Identifying sustainability communicators in urban regeneration: Integrating individual and relational attributes,” Journal of Cleaner Production, vol. 173, pp. 278–291, 2018.
31. A. Karczmarczyk, J. Jankowski, and J. Wątrobski, “Multi-criteria seed selection for targeted influence maximization within social networks,” in Computational Science–ICCS 2021: 21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part III, pp. 454–461, Springer, 2021.
32. A. Muruganantham and M. Gandhi, “Discovering and ranking influential users in social media networks using Multi-Criteria Decision Making (MCDM) Methods,” Indian J Sci Technol, vol. 9, no. 32, pp. 1–11, 2016.
33. A. Saxena and S. Iyengar, “Centrality measures in complex networks: A survey,” arXiv preprint https://arxiv.org/abs/2011.07190, 2020.
34. H. Lai and H. Liao, “A multi-criteria decision making method based on DNMA and CRITIC with linguistic D numbers for blockchain platform evaluation,” Engineering Applications of Artificial Intelligence, vol. 101, p. 104200, 2021.
35. I. Erol, I. M. Ar, and I. Peker, “Scrutinizing blockchain applicability in sustainable supply chains through an integrated fuzzy multi-criteria decision making framework,” Applied Soft Computing, vol. 116, p. 108331, 2022.
36. C. Öztürk and A. Yildizbaşi, “Barriers to implementation of blockchain into supply chain management using an integrated multi-criteria decision-making method: a numerical example,” Soft Computing, vol. 24, pp. 14771–14789, 2020.
37. M. Çolak, İ. Kaya, B. Özkan, A. Budak, and A. Karaşan, “A multi-criteria evaluation model based on hesitant fuzzy sets for blockchain technology in supply chain management,” Journal of Intelligent & Fuzzy Systems, vol. 38, no. 1, pp. 935–946, 2020.
38. A. O. Bielinskyi and V. N. Soloviev, “Complex network precursors of crashes and critical events in the cryptocurrency market,” in Ceur workshop proceedings, vol. 2292, pp. 37–45, 2018.
39. D. Lin, J. Wu, Q. Yuan, and Z. Zheng, “Modeling and understanding ethereum transaction records via a complex network approach,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 67, no. 11, pp. 2737–2741, 2020.
40. L. Serena, S. Ferretti, and G. D’Angelo, “Cryptocurrencies activity as a complex network: Analysis of transactions graphs,” Peer-to-Peer Networking and Applications, vol. 15, no. 2, pp. 839–853, 2022.
41. F. Dweiri, S. Kumar, S. A. Khan, and V. Jain, “Designing an integrated AHP based decision support system for supplier selection in automotive industry,” Expert Systems with Applications, vol. 62, pp. 273–283, 2016.
42. G. S. Mahendra, “Implementation of the FUCOM-SAW Method on E-Commerce Selection DSS in Indonesia,” Tech-E, vol. 5, no. 1, pp. 75–85, 2021.
43. E. P. Sarabi and S. A. Darestani, “Developing a decision support system for logistics service provider selection employing fuzzy MULTIMOORA & BWM in mining equipment manufacturing,” Applied Soft Computing, vol. 98, p. 106849, 2021.
44. R. Fahlepi, “Decision support systems employee discipline identification using the simple multi attribute rating technique (SMART) method,” Journal of Applied Engineering and Technological Science (JAETS), vol. 1, no. 2, pp. 103–112, 2020.
45. K. Kądziołka, “The promethee ii method in multi-criteria evaluation of cryptocurrency exchanges,” Economic and Regional Studies/Studia Ekonomiczne i Regionalne, vol. 14, no. 2, pp. 131–145, 2021.
46. Z. Aljinović, B. Marasović, and T. Šestanović, “Cryptocurrency portfolio selection—A multi-criteria approach,” Mathematics, vol. 9, no. 14, p. 1677, 2021.
47. S. Khan, M. Gulistan, N. Kausar, S. Kousar, D. Pamucar, and G. M. Addis, “Analysis of cryptocurrency market by using Q-rung orthopair fuzzy hypersoft set algorithm based on aggregation operators,” Complexity, vol. 2022, 2022.
48. G. Ilieva, T. Yankova, I. Radeva, and I. Popchev, “Blockchain software selection as a fuzzy multi-criteria problem,” Computers, vol. 10, no. 10, p. 120, 2021.
49. U. Hacioglu, D. Chlyeh, M. K. Yilmaz, E. Tatoglu, and D. Delen, “Crafting performance-based cryptocurrency mining strategies using a hybrid analytics approach,” Decision Support Systems, vol. 142, p. 113473, 2021.
50. Y.-C. Yang, W.-S. Shieh, and C.-Y. Lin, “Applying the Fuzzy BWM to Determine the Cryptocurrency Trading System under Uncertain Decision Process,” Axioms, vol. 12, no. 2, p. 209, 2023.
51. B. B. Gardas, A. Heidari, N. J. Navimipour, and M. Unal, “A fuzzy-based method for objects selection in blockchain-enabled edge-IoT platforms using a hybrid multi-criteria decision-making model,” Applied Sciences, vol. 12, no. 17, p. 8906, 2022.
52. I. M. Ar, I. Erol, I. Peker, A. I. Ozdemir, T. D. Medeni, and I. T. Medeni, “Evaluating the feasibility of blockchain in logistics operations: A decision framework,” Expert Systems with Applications, vol. 158, p. 113543, 2020.
53. F. Bloch, M. O. Jackson, and P. Tebaldi, “Centrality measures in networks,” Social Choice and Welfare, pp. 1–41, 2023.
54. Y. Du, C. Gao, Y. Hu, S. Mahadevan, and Y. Deng, “A new method of identifying influential nodes in complex networks based on TOPSIS,” Physica A: Statistical Mechanics and its Applications, vol. 399, pp. 57–69, 2014.
55. W. Zhang, Q. Zhang, and H. Karimi, “Seeking the important nodes of complex networks in product R&D team based on fuzzy AHP and TOPSIS,” Mathematical Problems in Engineering, vol. 2013, 2013.
56. P. Boldi and S. Vigna, “Axioms for centrality,” Internet Mathematics, vol. 10, no. 3-4, pp. 222–262, 2014.
57. W. Sałabun, “The Characteristic Objects Method: A New Distance-based Approach to Multicriteria Decision-making Problems,” Journal of Multi-Criteria Decision Analysis, vol. 22, no. 1-2, pp. 37–50, 2015.
58. S. Faizi, W. Sałabun, S. Ullah, T. Rashid, and J. Więckowski, “A New Method to Support Decision-Making in an Uncertain Environment Based on Normalized Interval-Valued Triangular Fuzzy Numbers and COMET Technique,” Symmetry, vol. 12, no. 4, p. 516, 2020.
59. S. Faizi, W. Sałabun, T. Rashid, S. Zafar, and J. Wątróbski, “Intuitionistic fuzzy sets in multi-criteria group decision making problems using the characteristic objects method,” Symmetry, vol. 12, no. 9, p. 1382, 2020.
60. W. Sałabun, A. Karczmarczyk, and J. Wątróbski, “Decision-making using the hesitant fuzzy sets COMET method: An empirical study of the electric city buses selection,” in 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1485–1492, IEEE, 2018.
61. B. Paradowski and Z. Drażek, ̨ “Identification of the decision-making model for selecting an information system,” Procedia Computer Science, vol. 176, pp. 3802–3809, 2020.
62. B. Kizielewicz, A. Shekhovtsov, and W. Sałabun, “pymcdm—the universal library for solving multi-criteria decision-making problems,” SoftwareX, vol. 22, p. 101368, 2023.
63. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
64. S. Kumar, F. Spezzano, V. Subrahmanian, and C. Faloutsos, “Edge weight prediction in weighted signed networks,” in Data Mining (ICDM), 2016 IEEE 16th International Conference on, pp. 221–230, IEEE, 2016.
65. S. Kumar, B. Hooi, D. Makhija, M. Kumar, C. Faloutsos, and V. Subrahmanian, “Rev2: Fraudulent user prediction in rating platforms,” in Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 333–341, ACM, 2018.

Uwagi

1. Thematic Tracks Regular Papers

2. 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-ece0c45a-9174-4fc7-bb6e-7808da0d7caa