Abstract
In this contribution we carry out an analysis of the Fuzzy Reasoning Methods for Fuzzy Rule Based Classification Systems in the framework of balanced and imbalanced data-sets with different degrees of imbalance. We analyze the behaviour of the Fuzzy Rule Based Classification Systems searching for the best type of Fuzzy Reasoning Method in each case, also studying the cooperation of some pre-processing methods of instances for imbalanced data-sets. To do so we use a fuzzy rule learning method that extends the well-known Wang and Mendel algorithm to classification problems.
The results obtained show the necessity to apply an instance pre-processing step and the differences for the most appropriate Fuzzy Reasoning Method in balanced and imbalanced data-sets, concluding that the choice of the Fuzzy Reasoning Method depends on the degree of imbalance, being the most adequate the use of the Winning Rule for high imbalanced data-sets and the Additive Combination method for the remaining data-sets.
Supported by Spanish Projects TIN-2005-08386-C05-01 & TIC-2005-08386-C05-03.
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
Barandela, R., Sánchez, J.S., García, V., Rangel, E.: Strategies for learning in class imbalance problems. Pattern Recognition 36(3), 849–851 (2003)
Batista, G.E.A.P.A., Prati, R.C., Monard, M.C.: A study of the behaviour of several methods for balancing machine learning training data. SIGKDD Explorations 6(1), 20–29 (2004)
Chawla, N.V., Japkowicz, N., Kolcz, A.: Editorial: special issue on learning from imbalanced data sets. SIGKDD Explorations 6(1), 1–6 (2004)
Chi, Z., Yan, H., Pham, T.: Fuzzy algorithms with applications to image processing and pattern recognition. World Scientific, Singapore (1996)
Cordón, O., del Jesús, M.J., Herrera, F.: A proposal on reasoning methods in fuzzy rule-based classification systems. International Journal of Approximate Reasoning 20(1), 21–45 (1999)
del Jesús, M.J., Fernández, A., García, S., Herrera, F.: A First Study on the Use of Fuzzy Rule Based Classification Systems for Problems with Imbalanced Data Sets. In: Symposium on Fuzzy Systems in Computer Science (FSCS06), Magdeburg, Germany, pp. 63–72 (2006)
Ishibuchi, H., Nakashima, T.: Effect of Rule Weights in Fuzzy Rule-Based Classification Systems. IEEE Transactions on Fuzzy Systems 9(4), 506–515 (2001)
Ishibuchi, H., Nakashima, T., Nii, M.: Classification and modeling with linguistic information granules: Advanced approaches to linguistic Data Mining. Springer, Berlin (2004)
Ishibuchi, H., Yamamoto, T.: Rule Weight Specification in Fuzzy Rule-Based Classification Systems. IEEE Trans. on Fuzzy Systems 13, 428–435 (2005)
Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man, and Cybernetics 25(2), 353–361 (1992)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fernández, A., García, S., del Jesús, M.J., Herrera, F. (2007). A Study on the Use of the Fuzzy Reasoning Method Based on the Winning Rule vs. Voting Procedure for Classification with Imbalanced Data Sets. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_46
Download citation
DOI: https://doi.org/10.1007/978-3-540-73007-1_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73006-4
Online ISBN: 978-3-540-73007-1
eBook Packages: Computer ScienceComputer Science (R0)