Abstract
An improvement of a fuzzy Artificial Neural Network model based on correlation coefficients is presented. As aggregation operator to compute the net input to target neuron a uninorm is used, instead of the sum of all the influences that the neuron receives usually used in typical artificial neurons. Such combination allows increasing the model performance in problem solving. While the natural framework and the interpretability presented in the former model are preserved by using fuzzy sets, experimental results show the improvement can be accomplished by using the proposed model. Significant differences of performance with the previous model in favor of the new one, and comparable results with a traditional classifier were statistically demonstrated. It is also remarkable that the model proposed shows a better behavior in presence of irrelevant attributes than the rest of tested classifiers.
This work was supported in part by VLIR (Vlaamse InterUniversitaire Raad, Flemish Interuniversity Council, Belgium) under the IUC Program VLIR-UCLV.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pal, S.K., Dillon, T.S., Yeung, D.S. (eds.): Soft Computing in Case Based Reasoning. Springer, London (2001)
García, M., Bello, P.R.: A Model and its different applications to Case-Based reasoning. Knowledge-based systems 9, 465–473 (1996)
Rodríguez, Y., et al.: Extending a Hybrid CBR-ANN Model by Modeling Predictive Attributes using Fuzzy Sets. In: Sichman, J.S., Coelho, H., Rezende, S.O. (eds.) IBERAMIA 2006 and SBIA 2006. LNCS (LNAI), vol. 4140, pp. 238–248. Springer, Heidelberg (2006)
Wilson, D.R., Martinez, T.R.: Improved Heterogeneous Distance Functions. Journal of Artificial Intelligence Research 6(1), 1–34 (1997)
Shiang, D., Lin, N.P.: Correlation of fuzzy sets. Fuzzy Sets and Systems 102, 221–226 (1999)
Shiang-Tai, L., Chiang, K.: Fuzzy measures for correlation coefficient of fuzzy numbers. Fuzzy Sets and Systems 128, 267–275 (2002)
Siegel, S., Castellan, N.J.: Nonparametric Statistics for the Behavioral Sciences. McGraw-Hill, New York (1988)
Sima, J.: Neural Expert System. Neural Networks 8(2) (1995)
De Baets, B., Fodor, J.: Residual Operators of Uninorms. Soft Computing 3, 89–100 (1999)
Yager, R.R., Rybalov, A.: Uninorm Agregation Operators. Fuzzy Sets and Systems 80, 111–120 (1996)
Tsadiras, A.K., Margaritis, K.G.: The MYCIN certainty factor handling function as uninorm operator and its use as a threshold function in artificial neurons. Fuzzy Sets and Systems 93, 263–274 (1998)
Murphy, P.M., Aha, D.W.: UCI Repository of Machine Learning Databases, http://www.ics.uci.edu/~mlearn/mlrepository.htm
Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: IJCAI-93, pp. 1022–1027 (1993)
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Hall, M.A., Smith, L.A.: Practical feature subset selection for machine learning. In: Proceedings of the 21st ACSC, pp. 181–191. Springer, Heidelberg (1998)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Rodriguez, Y., De Baets, B., Grau, M.M.G.R., Morell, C., Bello, R. (2007). Improving a Fuzzy ANN Model Using Correlation Coefficients. In: Castillo, O., Melin, P., Ross, O.M., Sepúlveda Cruz, R., Pedrycz, W., Kacprzyk, J. (eds) Theoretical Advances and Applications of Fuzzy Logic and Soft Computing. Advances in Soft Computing, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72434-6_76
Download citation
DOI: https://doi.org/10.1007/978-3-540-72434-6_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72433-9
Online ISBN: 978-3-540-72434-6
eBook Packages: EngineeringEngineering (R0)