TitleHandling fuzzy systems’ accuracy-interpretability trade-off by means of multi-objective evolutionary optimization methods – selected problems
Journal titleBulletin of the Polish Academy of Sciences: Technical Sciences
Divisions of PASNauki Techniczne
Date2015[2015.01.01 AD - 2015.12.31 AD]
ReferencesAlcalá (2011), A fuzzy association rule - based classification model for high - dimensional problems with genetic rule selection and lateral tuning on Fuzzy Systems, IEEE Trans, 19, 857. ; Fazzolari (2013), A review of the application of multiobjective evolutionary fuzzy systems : current status and further directions on Fuzzy Systems, IEEE Trans, 21, 45. ; Rudziński (null), A multi - objective genetic optimization of interpretability - oriented fuzzy rule - based classifiers ( to be published, Applied Soft Computing. ; Osowski (2013), Hoai Modified neuro - fuzzy TSK network and its application in electronic nose Pol, Bull Tech, 61, 675. ; Herrera (2008), Genetic fuzzy systems : taxonomy current research trends and prospects Evolutionary, Intelligence, 1, 27. ; Gorzałczany (2012), Genetic fuzzy rule - based modelling of dynamic systems using time series in, Lecture Notes Computer Science, 7269. ; Dubois (1996), What are fuzzy rules and how to use them and Systems, Fuzzy Sets, 84, 169, doi.org/10.1016/0165-0114(96)00066-8 ; Gorzałczany (2012), Accuracy vs interpretability of fuzzy rule - based classifiers : an evolutionary approach in, Lecture Notes Computer Science, 7269. ; Cios (2001), Medical Data Mining and Knowledge Discovery - Verlag Springer New York, Physica. ; Gacto (2011), Interpretability of linguistic fuzzy rule - based systems : an overview of interpretability measures, Information Sciences, 20, 181. ; Gorzałczany (2010), A modified Pittsburg approach to design a genetic fuzzy rule - based classifier from data in, Lecture Notes Computer Science, 6113. ; Zitzler (2001), Improving the strength pareto evolutionary algorithm for multi - objetive optimization Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, Proc, 1, 95. ; Smoczek (2014), TS fuzzy scheduling control system design using local pole placement and interval analysis Pol, Bull Tech, 62, 1. ; Deb (2002), A fast and elitist multiobjective genetic algorithm : NSGA - II on Evolutionary Computation, IEEE Trans, 6, 182. ; Baczyński (2008), Fuzzy Implications Studies in Fuzziness and Berlin, Soft Computing.