Abstract
Fuzzy Cognitive Map (FCM), which was first proposed by Kosko in 1986, is a novel tool for knowledge description and management. In this paper, we propose a new approach to mine the FCMs on the basis of data resource. First, raw data stored in the databases is preprocessed and converted to a fixed interval. Then, we adopt a novel hybrid optimization algorithm DE-SQP to produce the optimal weight matrix, whose goal is to make the weights fit the historical data best. At last, we apply the proposed method to solve a real-world problem. The experimental results show that the method is effective and efficient to mine the casual relationships hidden in the data resources by using the hybrid algorithm DE-SQP and the construction of FCMs gets rid of the intervention of domain experts, which guarantees the objectivity and completeness of the FCMs.
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References
Kosko, B.: Fuzzy Cognitive Maps. International Journal of Man-Machine Studies 24, 65–75 (1986)
Carvalho, J.P., Tome, A.B.: Qualitative Modelling of an Economic System using Rule-Based Fuzzy Cognitive Maps. Fuzzy Systems 2, 659–664 (2004)
Tan, C.O., Ozesmi, U.: A Generic Shallow Lake Ecosystem Model Based on Collective Expert Knowledge. Hydrobiologia 56, 125–142 (2006)
Georgopoulos, V.C., Malandraki, G.A., Stylios, C.D.: A Fuzzy Cognitive Map Approach to Deferential Diagnosis of Specific Language Impairment. Artificial Intelligence in Medicine 29, 261–278 (2003)
Lee, K.C., Lee, W.J., Kwon, O.B., Han, J.H., Yu, P.I.: Strategic Planning Simulation Based on Fuzzy Cognitive Map Knowledge and Differential Game. Simulation 71, 316–327 (2008)
Koulouriotis, D.E., Diakoulakis, I.E., Emiris, D.M.: Learning Fuzzy Cognitive Maps using Evolution Strategies: A Novel Schema for Modeling and Simulating High-lever Behavior. In: Proceedings of the Congress on Evolutionary Computation, pp. 364–371 (2001)
Papageorgiou, E.I., Groumpos, P.P.: Optimization of Fuzzy Cognitive Map Model in Clinical Radiotherapy through Differential Evolution Algorithm. Biomedical Soft Computing and Human Sciences 9, 25–31 (2003)
Parsopoulos, K.E., Papageorgiou, E.I., Groumpos, P.P., Vrahatis, M.N.: A First Study of Fuzzy Cognitive Maps Learning using Particle Swarm Optimization. In: Proceedings of the Congress on Evolutionary Computation, pp. 1440–1447 (2003)
Petalas, Y.G., Papageorgiou, E.I., Parsopoulos, K.E., Groumpos, P.P., Vrahatis, M.N.: Fuzzy Cognitive Maps Learning using Memetic Algorithms. In: Proceedings of the International Conference on Computational Methods in Sciences and Engineering, pp. 1420–1423 (2005)
Shou, W.H., Fan, W.H., Gao, Z.X., Liu, B.Y.: Hybrid Differential Evolution and Sequential Quadratic Programming Algorithm. In: Proceedings of the IEEE/ACIS International Conference on Computer and Information Science, pp. 3–6 (2011)
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Shou, W., Fan, W., Liu, B. (2012). A Data-Based Fuzzy Cognitive Map Mining Method Using DE-SQP Algorithm. In: Xiao, T., Zhang, L., Fei, M. (eds) AsiaSim 2012. AsiaSim 2012. Communications in Computer and Information Science, vol 325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34387-2_5
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DOI: https://doi.org/10.1007/978-3-642-34387-2_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34386-5
Online ISBN: 978-3-642-34387-2
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