Abstract
Determining the consensus of a collective is becoming a popular problem-solving method in our society. However, given that determining the consensus of large collectives is time-consuming, a multi-step consensus approach is necessary. Thus, one important problem is to determine the number of steps required to obtain a reliable consensus in an acceptable time. Execution time depends on the number of steps; determining the number of steps relies on the quality of the consensus in each step. The overall consensus quality depends on the problem of determining consensus in each step. Therefore, it is important to improve the consensus quality and investigate the quality according to the number of smaller collectives in each step. Herein, we improve the basic algorithm used for the multi-step consensus approach. The experiment result shows that the approach based on the improved algorithm is more efficient than that of the basic algorithm in terms of consensus quality (4.9%). Furthermore, the consensus quality was investigated according to the number of smaller collectives in each step.
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References
Dong, Y., et al.: Consensus reaching in social network group decision making: research paradigms and challenges. Knowl. Based Syst. 162, 3–13 (2018)
Nguyen, N.T.: Processing inconsistency of knowledge in determining knowledge of a collective. Cybern. Syst. Int. J. 40(8), 670–688 (2009)
Villaverde, A.F., et al.: A consensus approach for estimating the predictive accuracy of dynamic models in biology. Comput. Methods Programs Biomed. 119(1), 17–28 (2015)
Ali, A., Meilǎ, M.: Experiments with Kemeny ranking: what works when? Math. Soc. Sci. 64(1), 28–40 (2012)
Yang, B.: Bioinformatics analysis and consensus ranking for biological high throughput data. Ph.D. Dissertation, University of Paris 11 (2015)
Maleszka, M., Nguyen, N.T.: Integration computing and collective intelligence. Expert Syst. Appl. 42(1), 332–340 (2015)
Kozierkiewicz-Hetmańska, A.: Comparison of one-level and two-level consensuses satisfying the 2-optimality criterion. In: Nguyen, N.-T., Hoang, K., Jȩdrzejowicz, P. (eds.) ICCCI 2012. LNCS (LNAI), vol. 7653, pp. 1–10. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34630-9_1
Du Nguyen, V., Nguyen, N.T.: A two-stage consensus-based approach for determining collective knowledge. In: Le Thi, H.A., Nguyen, N.T., Van Do, T. (eds.) Advanced Computational Methods for Knowledge Engineering. AISC, vol. 358, pp. 301–310. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-17996-4_27
Nguyen, N.T.: Advanced Methods for Inconsistent Knowledge Management. Springer, London (2008). https://doi.org/10.1007/978-1-84628-889-0
Arrow, K.J.: Social Choice and Individual Values. Wiley, New York (1963)
Del Moral, M.J., Tapia, J.M., Chiclana, F., Al-Hmouz, A., Herrera-Viedma, E.: An analysis of consensus approaches based on different concepts of coincidence. J. Intell. Fuzzy Syst. 34(4), 2247–2259 (2018)
Amodio, S., D’Ambrosio, A., Siciliano, R.: Accurate algorithms for identifying the median ranking when dealing with weak and partial rankings under the Kemeny axiomatic approach. Eur. J. Oper. Res. 249(2), 667–676 (2016)
Kozierkiewicz-Hetmanska, A., Pietranik, M.: Assessing the quality of a consensus determined using a multi-level approach. In: IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), pp. 131–136. IEEE (2017)
Du Nguyen, V., Nguyen, N.T., Hwang, D.: An improvement of the two-stage consensus-based approach for determining the knowledge of a collective. In: Nguyen, N.-T., Manolopoulos, Y., Iliadis, L., Trawiński, B. (eds.) ICCCI 2016. LNCS (LNAI), vol. 9875, pp. 108–118. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45243-2_10
Dang, D.T., Du Nguyen, V., Nguyen, N.T., Hwang, D.: A three-stage consensus-based method for collective knowledge determination. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q.T. (eds.) Modern Approaches for Intelligent Information and Database Systems. SCI, vol. 769, pp. 3–14. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76081-0_1
Kozierkiewicz-Hetmańska, A., Sitarczyk, M.: The efficiency analysis of the multi-level consensus determination method. In: Nguyen, N.T., Papadopoulos, G.A., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds.) ICCCI 2017. LNCS (LNAI), vol. 10448, pp. 103–112. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67074-4_11
Kozierkiewicz-hetma, A., Marcin, P.: The knowledge increase estimation framework for ontology integration on the concept level. J. Intell. Fuzzy Syst. 32, 1161–1172 (2017)
Kozierkiewicz-Hetmańska, A.: Analysis of susceptibility to the consensus for a few representations of collective knowledge. Int. J. Softw. Eng. Knowl. Eng. 24(5), 759–775 (2014)
William, J.V., Joseph, P.W.: Statistics in Kinesiology, 4th edn. Human Kinetics (2012)
Acknowledgment
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2017R1A2B4009410).
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Dang, D.T., Nguyen, N.T., Hwang, D. (2019). Increasing the Quality of Multi-step Consensus. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11432. Springer, Cham. https://doi.org/10.1007/978-3-030-14802-7_1
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