Abstract
Interpretability of Mamdani fuzzy rule-based systems (MFRBSs) has been widely discussed in the last years, especially in the framework of multi-objective evolutionary fuzzy systems (MOEFSs). Here, multi-objective evolutionary algorithms (MOEAs) are applied to generate a set of MFRBSs with different trade-offs between interpretability and accuracy. In MOEFSs interpretability has often been measured in terms of complexity of the rule base and only recently partition integrity has also been considered. In this paper, we introduce a novel index for evaluating the interpretability of MFRBSs, which takes both the rule base complexity and the data base integrity into account. We discuss the use of this index in MOEFSs, which generate MFRBSs by concurrently learning the rule base, the linguistic partition granularities and the membership function parameters during the evolutionary process. The proposed approach has been experimented on six real world regression problems and the results have been compared with those obtained by applying the same MOEA, with only accuracy and complexity of the rule base as objectives. We show that our approach achieves the best trade-offs between interpretability and accuracy.
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Alcalá R, Alcalá-Fdez J, Herrera F, Otero J (2007a) Genetic learning of accurate and compact fuzzy rule based systems based on the 2-Tuples linguistic representation. Int J Approx Reason 44:45–64
Alcalá R, Gacto MJ, Herrera F, Alcalá-Fdez J (2007b) A multi-objective genetic algorithm for tuning and rule selection to obtain accurate and compact linguistic fuzzy rule-based systems. Int J Uncertain Fuzz Knowl Based Syst 15(5):521–537
Alcalá R, Ducange P, Herrera F, Lazzerini B, Marcelloni F (2009) A Multi-objective evolutionary approach to concurrently learn rule and data bases of linguistic fuzzy rule-based systems. IEEE Trans Fuzzy Syst 17(5):1106–1122
Alonso JM, Magdalena L, Guillaume S (2008) HILK: a new methodology for designing highly interpretable linguistic knowledge bases using the fuzzy logic formalism. Int J Intell Syst 23:761–794
Alonso JM, Magdalena L, González-Rodríguez G (2009) Looking for a good fuzzy system interpretability index: an experimental approach. Int J Approx Reason 51(1):115–134
Antonelli M, Ducange P, Lazzerini B, Marcelloni F (2009a) Learning concurrently partition granularities and rule bases of Mamdani fuzzy systems in a multi-objective evolutionary framework. Int J Approx Reason 50(7):1066–1080
Antonelli M, Ducange P, Lazzerini B, Marcelloni F (2009b) Multi-objective evolutionary learning of granularity, membership function parameters and rules of Mamdani fuzzy systems. Evol Intel 2(1–2):21–37
Botta A, Lazzerini B, Marcelloni F, Stefanescu D (2009) Context adaptation of fuzzy systems through a multi-objective evolutionary approach based on a novel interpretability index. Soft Comput 13(5):437–449
Casillas J, Cordón O, Herrera F (2002) COR: a methodology to improve ad hoc data-driven linguistic rule learning methods by inducing cooperation among rules. IEEE Trans Syst Man Cybern 32(4):526–537
Casillas J, Cordon O, Herrera F, Magdalena L (eds) (2003) Interpretability issues in fuzzy modeling. Springer, Heidelberg
Cococcioni M, Ducange P, Lazzerini B, Marcelloni F (2007) A Pareto-based multi-objective evolutionary approach to the identification of Mamdani fuzzy systems. Soft Comput 11(11):1013–1031
Coello Coello CA, Lamont GB (2004) Applications of multi-objective evolutionary algorithms. World Scientific, Singapore
Cordon O, Herrera F, Villar P (2001a) Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base. IEEE Trans Fuzzy Syst 9(4):667–674
Cordon O, Herrera F, Magadalena L, Villar P (2001b) A genetic learning process for the scaling factors, granularity and contexts of the fuzzy rule-based system data base. Inf Sci 136:85–107
de Oliveira JV (1999) Semantic constraints for membership function optimization. IEEE Trans Syst Man Cybern Part A 29(1):128–138
Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester
Ducange P, Lazzerini B, Marcelloni F (2009) Multi-objective genetic fuzzy classifiers for imbalanced and cost-sensitive datasets. Soft Comput 14(7):713–728
Gacto MJ, Alcalá R, Herrera F (2009) Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems. Soft Comput 13(5):419–436
Gacto MJ, Alcalá R, Herrera F (2010) Integration of an index to preserve the semantic interpretability in the multi-objective evolutionary rule selection and tuning of linguistic fuzzy systems. IEEE Trans Fuzzy Syst. doi:10.1109/TFUZZ.2010.2041008
González A, Pérez R (1999) SLAVE: a genetic learning system based on the iterative approach. IEEE Trans Fuzzy Syst 7:176–191
Guillaume S (2001) Designing fuzzy inference systems from data: An interpretability-oriented review. IEEE Trans Fuzzy Syst 9(3):426–443
Herrera F (2008) Genetic fuzzy systems: taxonomy, current research trends and prospects. Evol Intel 1:27–46
Ishibuchi H (2007) Multiobjective genetic fuzzy systems: review and future research direction. In: Proceedings of FUZZ-IEEE 2007 international conference on fuzzy systems, London, 23–26 July
Ishibuchi H, Nojima Y (2007) Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning. Int J Approx Reason 44(1):4–31
Ishibuchi H, Yamamoto T (2004) Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets Syst 141(1):59–88
Ishibuchi H, Murata T, Turksen IB (1997) Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems. Fuzzy Sets Syst 89(2):135–150
Klawonn F (2006) Reducing the number of parameters of a fuzzy system using scaling functions. Soft Comput 10(9):749–756
Knowles JD, Corne DW (2002) Approximating the non dominated front using the Pareto archived evolution strategy. Evol Comput 8(2):149–172
Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7(1):1–13
Massey FJ (1951) The Kolmogorov-Smirnov test for goodness of fit. J Am Stat Assoc 46(253):68–78
Mencar C, Fanelli AM (2008) Interpretability constraints for fuzzy information granulation. Inf Sci 178:4585–4618
Mencar C, Castellano G, Fanelli AM (2007) Distinguishability quantification of fuzzy sets. Inf Sci 177:130–149
Pedrycz W, Gomide F (2007) Fuzzy systems engineering: toward human-centric computing. Wiley-IEEE Press, NJ
Pulkkinen P, Koivisto H (2010) A dynamically constrained multiobjective genetic fuzzy system for regression problems. IEEE Trans Fuzzy Syst 18(1):161–177
Ruspini EH (1969) A new approach to clustering. Inform Control 15(1):22–32
Teng Y, Wang W (2004) Constructing a user-friendly ga-based fuzzy system directly from numerical data. IEEE Trans Syst Man Cybern B 34(5):2060–2070
Wang LX, Mendel JM (1992) Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 22(6):1414–1427
Zhou SM, Gan JQ (2008) Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling. Fuzzy Sets Syst 159:3091–3131
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Appendix
Appendix
In Tables 6, 7, we show the averages and the standard deviations of the MSEs on training and test sets \( \left( {\overline{{{\text{MSE}}_{\text{TR}} }} (\sigma_{\text{TR}} )} \right. \) and \( \overline{{{\text{MSE}}_{\text{TS}} }} (\sigma_{\text{TS}} ), \) respectively), the results of the Kolmogorov–Smirnov test (column k–s TR and k–s TS for the training and test sets, respectively), and the averages and the standard deviations of the interpretability index \( \bar{I}\left( {\bar{I}(\sigma_{I} )} \right) \) for the MEDIAN and LAST solutions, respectively.
In Tables 8 and 9, we show the averages and the standard deviations of the complexity \( \left( {\overline{\text{COMP}} (\sigma_{\text{COMP}} )} \right), \) of the number of concrete rules \( \left( {\overline{{M^{\text{c}} }} (\sigma_{{M^{\text{c}} }} )} \right), \) and of the concrete \( \left( {\overline{{D^{\text{c}} }} (\sigma_{{D^{\text{c}} }} )} \right) \) and virtual \( \left( {\overline{{D^{\text{v}} }} (\sigma_{{D^{\text{v}} }} )} \right) \) dissimilarities for the MEDIAN and LAST solutions, respectively.
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Antonelli, M., Ducange, P., Lazzerini, B. et al. Learning concurrently data and rule bases of Mamdani fuzzy rule-based systems by exploiting a novel interpretability index. Soft Comput 15, 1981–1998 (2011). https://doi.org/10.1007/s00500-010-0629-4
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DOI: https://doi.org/10.1007/s00500-010-0629-4