Abstract
In this paper, a rule learning method based on the evidential C-means clustering is proposed to efficiently design a compact belief rule-based classification system. In this method, the evidential C-means algorithm is first used to obtain credal partitions of the training set. The clustering process operates in a supervised way by means of weighted product-space clustering with the goals of obtaining both good inter-cluster separability and inner-cluster pureness. Then the antecedent part of a belief rule is defined by projecting each multi-dimensional credal partition onto each feature. The consequent class and the weight of each belief rule are identified by combing those training patterns belonging to each hard credal partition within the framework of belief functions. An experiment based on several real data sets was carried out to show the effectiveness of the proposed method.
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
References
Aggarwal, C.C.: Data Classification: Algorithm and Applications. Chapman & Hall, Boca Raton (2014)
Akbarzadeh-Totonchi, M.R., Moshtagh-Khorasani, M.: A hierarchical fuzzy rule-based approach to aphasia diagnosis. J. Biomed. Inform. 40, 465–475 (2007)
Almeida, R.J., Denoeux, T., Kaymak, U.: Constructing rule-based models using the belief functions framework. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds.) IPMU 2012. CCIS, vol. 299, pp. 554–563. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31718-7_57
Chi, Z., Yan, H., Pham, T.: Fuzzy Algorithms with Applications to Image Processing and Pattern Recognition. World Scientific, Singapore (1996)
Dempster, A.: Upper and lower probabilities induced by multivalued mapping. Ann. Math. Statist. 38, 325–339 (1967)
Ishibuchi, H., Nozaki, K., Tanaka, H.: Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets Syst. 52, 21–32 (1992)
Jiao, L., Denœux, T., Pan, Q.: A hybrid belief rule-based classification system based on uncertain training data and expert knowledge. IEEE Trans. Syst. Man Cybern. Syst. 46(12), 1711–1723 (2016)
Jiao, L., Pan, Q., Denœux, T., Liang, Y., Feng, X.: Belief rule-based classification system: extension of FRBCS in belief functions framework. Inform. Sci. 309(1), 26–49 (2015)
Masson, M.H., Denoeux, T.: Clustering interval-valued data using belief functions. Pattern Recogn. Lett. 25, 163–171 (2004)
Samantaray, S.R.: Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection. Appl. Soft Comput. 13, 928–938 (2013)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
Stavrakoudis, D.G., Galidaki, G.N., Gitas, I.Z., Theocharis, J.B.: A genetic fuzzy-rule-based classifier for land cover classification from hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 50, 130–148 (2012)
Tsang, C., Kwong, S., Wang, H.: A systematic fuzzy rule based approach for fault classification in transmission lines. Pattern Recogn. 40, 2373–2391 (2007)
Wu, H., Mendel, J.: Classification of battlefield ground vehicles using acoustic features and fuzzy logic rule-based classifiers. IEEE Trans. Fuzzy Syst. 15, 56–72 (2007)
Acknowledgments
This work is partially supported by China Natural Science Foundation (Nos. 61790552 and 61672431).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Jiao, L., Geng, X., Pan, Q. (2018). A Compact Belief Rule-Based Classification System with Evidential Clustering. In: Destercke, S., Denoeux, T., Cuzzolin, F., Martin, A. (eds) Belief Functions: Theory and Applications. BELIEF 2018. Lecture Notes in Computer Science(), vol 11069. Springer, Cham. https://doi.org/10.1007/978-3-319-99383-6_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-99383-6_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99382-9
Online ISBN: 978-3-319-99383-6
eBook Packages: Computer ScienceComputer Science (R0)