Abstract
Interpretability is one of the most important driving forces for the adoption of fuzzy rule-based classifiers. However, it is not given for granted, especially when fuzzy models are acquired from data. Therefore, evaluation of interpretability should be regarded as a major research topic. In this paper, we describe a technique for automatic interpretability assessment, based on the co-intension of the semantics of the knowledge base with the intrinsic semantics designated by linguistic labels. The core of the evaluation technique relies on the propositional view of rules and on logical operations. An illustrative example shows how the proposed approach can be useful in detecting lacks of interpretability for a simple knowledge base.
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References
Mencar, C.: Theory of Fuzzy Information Granulation: Contributions to Interpretability Issues. PhD thesis, University of Bari, Italy (2005)
Riid, A., Rustern, E.: Interpretability of Fuzzy Systems and Its Application to Process Control. In: Proc. of Fuzzy Systems IEEE International Conference, 2007, FUZZ-IEEE 2007, pp. 1–6 (2007)
Michalski, R.S.: A theory and methodology of inductive learning. Technical Report UIUCDCS-R-83-1122, Department of Computer Science, University of Illinois, Urbana (January 1983)
Mencar, C., Castellano, G., Fanelli, A.M.: On the role of interpretability in fuzzy data mining. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS) 15(5), 521–537 (2007)
Zadeh, L.A.: Is there a need for fuzzy logic? Information Sciences 178, 2751–2779 (2008)
Alonso, J.M., Magdalena, L., Guillaume, S.: HILK: A new methodology for designing highly interpretable linguistic knowledge bases using the fuzzy logic formalism. International Journal of Intelligent Systems 23, 761–794 (2008)
McCluskey, E.J.: Minimization of Boolean functions. Bell Labs Technical Journal 35(5), 1417–1444 (1956)
UCI Machine Learning Repository Content Summary, http://www.ics.uci.edu/~mlearn/MLSummary.html
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Mencar, C., Castiello, C., Fanelli, A.M. (2009). Interpretability Assessment of Fuzzy Rule-Based Classifiers. In: Di Gesù, V., Pal, S.K., Petrosino, A. (eds) Fuzzy Logic and Applications. WILF 2009. Lecture Notes in Computer Science(), vol 5571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02282-1_20
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DOI: https://doi.org/10.1007/978-3-642-02282-1_20
Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-642-02282-1
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