Abstract
It is known that the non-linear integral has been generally used as an aggregation operator in classification problems, because it represents the potential interaction of a group of attributes. The lower integral is a type of non-linear integral with respect to non-additive set functions, which represents the minimum potential of efficiency for a group of attributes with interaction. Through solving a linear programming problem, the value of lower integral could be calculated. When we consider the lower integral as a classifier, the difficult step is the learning of the non-additive set function, which is used in lower integral. Then, the Extreme Learning Machine technique is applied to solve the problem and the ELM lower integral classifier is proposed in this paper. The implementations and performances of ELM lower integral classifier and single lower integral classifier are compared by experiments with six data sets.
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References
Sugeno, M.: Theory of fuzzy integrals and its applications. Doct. Thesis. Tokyo Institute of Technology (1974)
Sugeno, M., Murofushi, T.: Choquet integral as an integral form for a general class of fuzzy measures. In: 2nd IFSA Congress, Tokyo, pp. 408–411 (1987)
Wang, Z., Li, W., Lee, K.H., Leung, K.S.: Lower integrals and upper integrals with respect to non-additive set functions. Fuzzy Sets and Systems 159, 646–660 (2008)
Grabisch, M., Sugeno, M.: Multi-attribute classification using fuzzy integral. In: 1st IEEE Int. Conf. on Fuzzy Systems, San Diego, pp. 47–54 (March 8-12, 1992)
Tahani, H., Keller, J.M.: Information fusion in computer vision using fuzzy integral. IEEE Trans. SMC 20(3), 733–741 (1990)
Grabisch, M., Nicolas, J.M.: Classification by fuzzy integral. Performance and Tests: Fuzzy Sets and Systems 65, 255–271 (1994)
Grabisch, M.: The application of fuzzy integrals in multicriteria decision making. European Journalof Operational Research 89, 445–456 (1996)
Grabisch, M.: Fuzzy integral in multicriteria decision making. Fuzzy Sets and Systems (Special Issue on 5th IFSA Congress) 69, 279–298. (1995)
Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems 1, 3–28 (1978)
Dubois, D., Prade, H.: Possibility Theory. Plenum Press, New York (1988)
Wang, Z., Klir, G.J.: Fuzzy measure theory. New York, Plenum Press (1992)
Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: Theory and applications. Neurocomputing 70, 489–501 (2006)
Wang, X., Chen, A., Feng, H.: Upper integral network with extreme learning mechanism. Neurocomputing 74, 2520–2525 (2011)
Chen, A., Liang, Z., Feng, H.: Classification Based on Upper Integral. In: Proceedings of 2011 International Conference on Machine Learning and Cybernetics 2, pp. 835–840 (2011)
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Chen, A., Feng, H., Guo, Z. (2014). Classification Based on Lower Integral and Extreme Learning Machine. In: Wang, X., Pedrycz, W., Chan, P., He, Q. (eds) Machine Learning and Cybernetics. ICMLC 2014. Communications in Computer and Information Science, vol 481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45652-1_5
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DOI: https://doi.org/10.1007/978-3-662-45652-1_5
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