اخبار و اعلانات
آمار
| تعداد نشریات | 29 |
| تعداد شماره‌ها | 629 |
| تعداد مقالات | 6,353 |
| تعداد مشاهده مقاله | 9,719,040 |
| تعداد دریافت فایل اصل مقاله | 6,357,403 |
Heuristics-based modelling of human decision process | ||
| Iranian Journal of Fuzzy Systems | ||
| دوره 20، شماره 3، مرداد و شهریور 2023، صفحه 19-30 اصل مقاله (820.85 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2023.7636 | ||
| نویسندگان | ||
| M. Aggarwal* 1، 2؛ A. F. Tehrani3 | ||
| 1School of Artificial Intelligence and Data Science, IIT Jodhpur, Jodhpur, India | ||
| 2Digital Humanities, IIT Jodhpur, Jodhpur, India | ||
| 3Hof University of Applied Sciences, Hof, Germany | ||
| چکیده | ||
| Attitudinal Choquet integral (ACI) is a recent aggregation operator that considers in the aggregation process the criteria interaction and the DM's attitude, both of which are specific to the decision-maker. However, this capability comes at the cost of increased complexity that hinders its applicability in big data analytics. To address the same, in this paper, we explore some heuristics-based forms of the ACI operator, so as to somehow overcome its complexity. We devise new and efficient forms of $\mathcal{ACI}$, and test their validity in the real world datasets, against the backdrop of preference learning. | ||
| کلیدواژه‌ها | ||
| Attitudinal Choquet integral؛ efficiency؛ complexity reduction؛ attitudinal character؛ multi criteria decision making | ||
| مراجع | ||
|
[1] M. Aggarwal, Compensative weighted averaging aggregation operators, Applied Soft Computing, 28 (2015), 368-378.
[2] M. Aggarwal, Generalized compensative weighted averaging aggregation operators, Computers and Industrial Engineering, 87(2015), 81-90.
[3] M. Aggarwal, Attitudinal choquet integrals and applications in decision making, International Journal of Intelligent Systems, 33(4) (2018), 879-898.
[4] M. Aggarwal, Logit choice models for interactive attributes, Information Sciences, 507 (2020), 298-312.
[5] M. Aggarwal, A. Fallah Tehrani, Modelling human decision behaviour with preference learning, INFORMS Journal on Computing, 31(2) (2019), 318-334.
[6] M. Aggarwal, M. Hanmandlu, K. K. Biswas, Choquet integral vs. topsis: An intuitionistic fuzzy approach, In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), (2013), 1-8.
[7] L. Berrah, G. Mauris, J. Montmain, Monitoring the improvement of an overall industrial performance based on a choquet integral aggregation, Omega, 36(3) (2008), 340-351.
[8] G. Choquet, Annales de l institut Fourier}, Theory of Capacities, 5 (1953), 131-295.
[9] H. Daniels, B. Kamp, Applications of MLP networks to bond rating and house pricing, Neural Computation and Applications, 8 (1999), 226-234.
[10] H. Dyckhoff, W. Pedrycz, Generalized means as a model of compensation connectives, Fuzzy Sets and Systems, 14 (1984), 143-154.
[11] V. Fragnelli, S. Moretti, A game theoretical approach to the classification problem in gene expression data analysis, Computers and Mathematics with Applications, 55(5) (2008), 950-959.
[12] M. Grabisch, Fuzzy integral in multicriteria decision making, Fuzzy Sets and Systems, 69(3)(1995), 279-298.
[13] M. Grabisch, The application of fuzzy integrals in multicriteria decision making, European Journal of Operational Research, 89 (1996), 445-456.
[14] M. Grabisch, Fuzzy measures and integrals - theory and applications, chapter fuzzy integral for classification and feature extraction, Physica Verlag, (2000), 415-434.
[15] M. Grabisch, J. Duchene, F. Lino, P. Perny, Subjective evaluation of discomfort in sitting position, Fuzzy Optimization and Decision Making, 1(3) (2002), 287-312.
[16] M. Grabisch, J. L. Marichal, R. Mesiar, E. Pap, Aggregation functions: Means, Information Sciences, 181 (2011), 1-22.
[17] L. Jin, R. Mesiar, R. R. Yager, Melting probability measure with owa operator to generate fuzzy measure: The crescent method, IEEE Transactions on Fuzzy Systems, 27(6) (2019), 1309-1316.
[18] L. Jin, R. Mesiar, R. R. Yager, Derived fuzzy measures and derived choquet integrals with some properties, IEEE Transactions on Fuzzy Systems, 29(5) (2021), 1320-1324.
[19] L. Jin, R. Mesiar, R. R. Yager, The properties of crescent preference vectors and their utility in decision making with risk and preferences, Fuzzy Sets and Systems, 409 (2021), 114-117.
[20] R. Krishnapuram, J. Lee, Fuzzy-connective-based hierarchical aggregation networks for decision making, Fuzzy Sets and Systems, 46(1) (1992), 11-27.
[21] D. Liginlal, T. T. Ow, On policy capturing with fuzzy measures, European Journal of Operational Research, 167 (2005), 461-474.
[22] M. K. Luhandjula, Compensatory operators in fuzzy linear programming with multiple objectives, Fuzzy Sets and Systems, 8(3) (1982), 245-252.
[23] R. Mesiar, A. Mesiarova-Zemankova, K. Ahmad, Discrete choquet integral and some of its symmetric extensions, Fuzzy Sets and Systems, 184 (2011), 148-155.
[24] W. Nather, K. Walder, {\it Applying fuzzy measures for considering interaction effects in root dispersal models}, Fuzzy Sets and Systems, {\bf 158} (2007), 572-582.
[25] C. Rao, M. Gao, J. Wen, M. Goh, Multi-attribute group decision making method with dual comprehensive clouds under information environment of dual uncertain z-numbers, Information Sciences, 602 (2022), 106-127.
[26] K. Saito, Y. Watanabe, H. Hashimoto, A. Uchiyama, An application of fuzzy integral model for the clinical diagnosis, Journal of Biomedical Fuzzy Systems Association, 1(1) (2007), 17-24.
[27] A. F. Tehrani, W. Cheng, K. Dembczyski, E. Hullermeier, Learning monotone nonlinear models using the Choquet integral, Machine Learning, 89(1-2) (2012), 183-211.
[28] V. Torra, Y. Narukawa, The h-index and the number of citations: Two fuzzy integrals, IEEE Transactions on Fuzzy Systems, 16(3) (2008), 795-797.
[29] Z. Xu, R. R. Yager, Power-geometric operators and their use in group decision making, IEEE Transactions on Fuzzy Systems, 18(1) (2010), 94-105.
[30] R. R. Yager, On ordered weighted averaging aggregation operators in multi-criteria decision making, IEEE Transactions on Systems, Man and Cybernetics, 18 (1988), 183-190.
[31] M. Zhang, H. Guo, M. Sun, S. Liu, J. Forrest, A novel flexible grey multivariable model and its application in forecasting energy consumption in China, Energy, {\bf 239} (2022). DOI: 10.1016/j.energy.2021.122441.
[32] H. J. Zimmermann, P. Zysno, Latent connectives in human decision making, Fuzzy Sets and Systems, 4 (1980), 37-51.
[33] H. J. Zimmermann, P. Zysno, Decisions and evaluations by hierarchical aggregation of information, Fuzzy Sets and Systems, 10(1-3) (1983), 243-260. | ||
|
آمار تعداد مشاهده مقاله: 130 تعداد دریافت فایل اصل مقاله: 230 |
||