Handling fuzzy systems’ accuracy-interpretability trade-off by means of multi-objective evolutionary optimization methods – selected problems

• Autorzy: Gorzałczany M. B. , Rudziński F.

Identyfikatory

DOI

10.1515/bpasts-2015-0090

• Języki publikacji: EN

Abstrakty

EN

The paper addresses several open problems regarding the automatic design of fuzzy rule-based systems (FRBSs) from data using multi-objective evolutionary optimization algorithms (MOEOAs). In particular, we propose: a) new complexity-related interpretability measure, b) efficient strong-fuzzy-partition implementation for improving semantics-related interpretability, c) special-coding-free implementation of rule base and original genetic operators for its processing, and d) implementation of our ideas in the context of well-known MOEOAs such as SPEA2 and NSGA-II. The experiments demonstrate that our approach is an effective tool for handling FRBSs’ accuracy-interpretability trade-off, i.e, designing FRBSs characterized by various levels of such a trade-off (in particular, for designing highly interpretability-oriented systems of still competitive accuracy).

Słowa kluczowe

EN

accuracy and interpretability of fuzzy rule-based systems; multi-objective evolutionary optimization; genetic computations; fuzzy systems

PL

dokładność systemów rozmytych; optymalizacja; obliczenia genetyczne; systemy rozmyte

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2015
• Tom: Vol. 63, nr 3
• Strony: 791--798
• Opis fizyczny: Bibliogr. 22 poz., tab., wykr.

Twórcy

autor

Gorzałczany M. B.

• Department of Electrical and Computer Engineering, Kielce University of Technology, 7 1000-lecia P.P. Ave., 25-314 Kielce, Poland

autor

Rudziński F.

• Department of Electrical and Computer Engineering, Kielce University of Technology, 7 1000-lecia P.P. Ave., 25-314 Kielce, Poland

Bibliografia

• [1] K.J. Cios, Medical Data Mining and Knowledge Discovery, Physica-Verlag, Springer, New York, 2001.
• [2] O. Maimon and L. Rokach, Data Mining and Knowledge Discovery Handbook, Springer, New York, 2005.
• [3] J. Ponce and A. Karahoca, Data Mining and Knowledge Discovery in Real Life Applications, IN-TECH, Vienna, 2009.
• [4] D. Dubois and H. Prade, “What are fuzzy rules and how to use them”, Fuzzy Sets and Systems 84 (2), 169–185 (1996).
• [5] M.J. Gacto, R. Alcala, and F. Herrera, “Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures”, Information Sciences 181 (20), 4340–4360 (2011).
• [6] F. Herrera, “Genetic fuzzy systems: taxonomy, current research trends and prospects”, Evolutionary Intelligence 1 (1), 27–46 (2008).
• [7] M.B. Gorzałczany and F. Rudziński, “A modified Pittsburg approach to design a genetic fuzzy rule-based classifier from data”, Lecture Notes in Computer Science 6113, 88–96 (2010).
• [8] M.B. Gorzałczany and F. Rudziński, “Accuracy vs. interpretability of fuzzy rule-based classifiers: an evolutionary approach”, Lecture Notes in Computer Science 7269, 222–230 (2012).
• [9] M.B. Gorzałczany and F. Rudziński, “Genetic fuzzy rule-based modelling of dynamic systems using time series”, Lecture Notes in Computer Science 7269, 231–239 (2012).
• [10] M.B. Gorzałczany and F. Rudziński, “Measurement data in genetic fuzzy modelling of dynamic systems”, Measurements, Automatics, Control 12, 1420–1423 (2010), (in Polish).
• [11] F. Rudziński and J. Piekoszewski, “The maintenance costs estimation of electrical lines with the use of interpretability-oriented genetic fuzzy rule-based systems”, Przegląd Elektrotechniczny 8, 43–47 (2013).
• [12] M. Fazzolari, R. Alcala, Y. Nojima, H. Ishibuchi, and F. Herrera, “A review of the application of multiobjective evolutionary fuzzy systems: current status and further directions”, IEEE Trans. on Fuzzy Systems 21 (1), 45–65 (2013).
• [13] E.H. Ruspini, “A new approach to clustering”, Information and Control 15 (1), 22–32 (1969).
• [14] E. Zitzler, M. Laumanns, and L. Thiele, “SPEA2: Improving the strength pareto evolutionary algorithm for multi-objetive optimization”, Proc. Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems 1, 95–100 (2001).
• [15] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II”, IEEE Trans. on Evolutionary Computation 6 (2), 182–197 (2002).
• [16] J. Alcalá-Fdez, R. Alcalá, and F. Herrera, “A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning”, IEEE Trans. on Fuzzy Systems 19 (5), 857–872 (2011).
• [17] F. Rudziński, “A multi-objective genetic optimization of interpretability-oriented fuzzy rule-based classifiers”, Applied Soft Computing, (to be published).
• [18] M. Baczyński and B. Jayaram, Fuzzy Implications, Studies in Fuzziness and Soft Computing, Springer, Berlin, 2008.
• [19] Machine Learning Database Repository, University of California at Irvine, (ftp.ics.uci.edu).
• [20] L.-X. Wang, A Course in Fuzzy Systems and Control, Prentice-Hall, New York, 1998.
• [21] S. Osowski, K. Brudzewski, and L. Tran Hoai, “Modified neuro-fuzzy TSK network and its application in electronic nose”, Bull. Pol. Ac.: Tech. 61 (3), 675–680 (2013).
• [22] J. Smoczek, “P1-TS fuzzy scheduling control system design using local pole placement and interval analysis”, Bull. Pol. Ac.: Tech. 62 (3), 455–464 (2014).