Abstract
A trust measurement method called certified belief in strength (CBS) for Extreme Learning Machine (ELM) Multi Agent Systems (MAS) is proposed in this paper. The CBS method is used to improve the performance of the individual agents of the MAS, i.e., ELM neural network. Then, trust measurement is achieved based on reputation and strength of the individual agents. In addition, trust is assemble from strong elements that are associated with the CBS which let the ELM to improve the performance of the MAS. The efficiency of the ELM-MAS-CBS model is verified with several activation function using benchmark datasets which are Pima Indians Diabetes (PID), Iris and Wine. The results show that the proposed ELM-MAS-CBS model is able to achieve better accuracy as compared with other approaches.
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References
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme Learning Machine: a new learning scheme of feedforward neural networks. In: Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 2004), Budapest, Hungary, July 25-29, pp. 985–990 (2004)
Huang, G.B., Zhu, Q.Y., Mao, K., Siew, C.K., Saratchandran, P., Sundararajan, N.: Can threshold networks be trained directly? IEEE Trans. Circuits Syst II 53(3), 187–191 (2006)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme Learning Machine: theory and applications. Neurocomputing 70(1), 489–501 (2006)
Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme Learning Machine for regression and multiclass classification. IEEE Trans. Systems, Man, and Cybernetics, Part B: Cybernetics 42(2), 513–529 (2012)
Huang, G.B., Chen, L.: Convex incremental Extreme Learning Machine. Neurocomputing 70(168), 3056–3062 (2007)
Huang, G.B., Chen, L.: Enhanced random search based incremental Extreme Learning Machine. Neurocomputing 71(16), 3460–3468 (2008)
Yap, K.S., Yap, H.J.: Daily Maximum Load Forecasting of Consecutive National Holidays using OSELM-Based Multi-Agents System with Average Strategy. Neurocomputing 81, 108–112 (2012)
Zhao, G., Shen, Z., Miao, C., Gay, R.: Enhanced Extreme Learning Machine with stacked generalization. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 1191–1198 (2008)
Sun, Z.L., Choi, T.M., Au, K.F., Yu, Y.: Sales forecasting using Extreme Learning Machine with applications in fashion retailing. Decision Support Systems 46(1), 411–419 (2008)
Lan, Y., Soh, Y.C., Huang, G.B.: Ensemble of online sequential Extreme Learning Machine. Neurocomputing 72(135), 3391–3395 (2009)
van Heeswijk, M., Miche, Y., Lindh-Knuutila, T., Hilbers, P.A.J., Honkela, T., Oja, E., Lendasse, A.: Adaptive ensemble models of extreme learning machines for time series prediction. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009, Part II. LNCS, vol. 5769, pp. 305–314. Springer, Heidelberg (2009)
Heeswijk, M.V., Miche, Y., Oja, E., Lendasse, A.: GPU-accelerated and parallelized ELM ensembles for large-scale regression. Neurocomputing 74(16), 2430–2437 (2011)
Quteishet, A., Lim, C.P., Tweedale, J., Jain, L.C.: A Neural Network-based Multi-agent Classifier System. Neurocomputing 72, 1639–1647 (2009)
Gwebu, K., Wang, J., Troutt, M.D.: Constructing a Multi-Agent System: An Architecture for a Virtual Marketplace. In: Phillips-Wren, G., Jain, L. (eds.) Intelligent Decision Support Systems in Agent-Mediated Environments. IOS Press (2005)
Hudson, D.L., Cohen, M.E.: Use of Intelligent Agents in the Diagnosis of Cardiac Disorders. Computers in Cardiology, 633–636 (2002)
Tolk, A.: An Agent-Based Decision Support System Architecture for the Military Domain. In: Phillips-Wren, G., Jain, L. (eds.) Intelligent Decision Support Systems in Agent-Mediated Environments. IOS Press (2005)
Ossowski, S., Fernandez, A., Serrano, J.M., Hernandez, J.Z., Garcia-Serrano, A.M., Perez-de-la-Cruz, J.L., Belmonte, M.V., Maseda, J.M.: Designing Multi agent Decision Support System the Case of Transportation Management. In: The 3rd International Joint Conference on Autonomous Agents and Multi agent Systems, pp. 1470–1471 (2004)
Singh, R., Salam, A., Lyer, L.: Using Agents and XML for Knowledge Representation and Exchange: An Intelligent Distributed Decision Support Architecture. In: The 9th Americans Conference on Information Systems, pp. 1854–1863 (2003)
Ossowski, S., Hernandez, J.Z., Iglesias, C.A., Ferndndez, A.: Engineering Agent Systems for Decision Support. In: The 3rd International Workshop Engineering Societies in the Agents World, pp. 184–198 (2002)
Quteishet, A., Lim, C.P., Saleh, J.M., Tweedale, J., Jain, L.C.: A Neural Network-based Multi-agent Classifier System with a Bayesian Formalism for Trust Measurement. Soft. Compt. 15(2), 221–231 (2001)
Mohammed, M.F., Lim, C.P., Quteishat, A.: A Novel Trust Measurement Method Based on Certified in Strength for a Multi-Agent Classifier System. Springer, London (2012)
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Yaw, C.T., Yap, K.S., Yap, H.J., Ungku Amirulddin, U.A. (2014). An ELM Based Multi Agent Systems Using Certified Belief in Strength. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham. https://doi.org/10.1007/978-3-319-12643-2_56
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DOI: https://doi.org/10.1007/978-3-319-12643-2_56
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