[1] Bodenhofer U., “Tuning Of Fuzzy Systems Using Genetic Algorithms”, A thesis submitted to ProfErich Peter Klement in partial satisfaction of the requirements for the obtainment of the academic degree of Diplom Ingenieur der Studienrichtung Technische Mathematik, 1996.
[2] Cordón O., “A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems”, International Journal of Approximate Reasoning 52 (6):894–913, 2011, https://doi.org/10.1016/j.ijar.2011.03.004.
[3] De Jong K. A., “Learning with genetic algorithms: an overview”, Machine Learning 3 (3):121–138, 1988, https://doi.org/10.1007/BF00113894.
[4] Casillas J., Cordón O., Herrera F., Magdalena L. (Eds.), Interpretability Issues in Fuzzy Modeling, Springer, 2003, https://doi.org/10.1007/978-3-540-37057-4.
[5] Ishibuchi H., Nakashima T., Murata T., “comparison of the Michigan and Pittsburgh approach to the design of classification systems”, electronic and communications in japan 80 (12):10-19, 1997, http://dx.doi.org/10.1002/(SICI)1520-6440(199712)80:123.0.CO;2-W.
[6] Fernández A., del Jesus M. J., Herrera F., “On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-sets”, Information Sciences 180 (8):1268–1291, 2010, https://doi.org/10.1016/j.ins.2009.12.014.
[7] Tan P.-N., Steinbach M. S., Karpatne A., Kumar V., Introduction to Data Mining, Pearson, 2nd Edition, 2019.
[8] Verma N.K., Singh V., Rajurkar S., Aqib M., “Fuzzy Inference Network with Mamdani Fuzzy Inference System”, In: Verma N., Ghosh A. (eds) Computational Intelligence: Theories, Applications and Future Directions -Volume I. Advances in Intelligent Systems and Computing, vol 798. Springer, 2019, https://doi.org/10.1007/978-981-13-1132-1_29.
[9] Kumar A., Venkateswaran J., “Reverse Sequential Covering Algorithm for Medical Data Mining“, Procedia Computer Science 47:109-117, 2015, http://dx.doi.org/10.1016/j.procs.2015.03.189.
[10] Wang L.-X., Mendel J. M., “Generating fuzzy rules by learning from examples”, IEEE Transactions on Systems, Man, and Cybernetics 22(6):1414-1427, 1992, https://doi.org/10.1109/21.199466.
[11] Sanz J. A., Fernández A., Sola H. B., Herrera F., “A genetic tuning to improve the performance of Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets”, International Journal of Approximate Reasoning 52(6):751–766, 2011, https://doi.org/10.1016/j.ijar.2011.01.011.
[12] Cordona O., Gomide F. A. C., Herreraa F., Hoffmann F., Magdalenad L., “Ten years of genetic fuzzy systems: current framework and new trends”, Fuzzy Sets and Systems, 141(1):5–31, 2004, https://doi.org/10.1016/S0165-0114(03)00111-8.
[13] Casillas J., Cordón O., del Jesus M. J., Herrera F., “Genetic Tuning of Fuzzy Rule Deep Structures Preserving Interpretability and Its Interaction With Fuzzy Rule Set Reduction”, IEEE Transaction on Fuzzy Systems 13(1):13-29, 2005, https://doi.org/10.1109/TFUZZ.2004.839670.
[14] Ishibuchi H., Nakashima T., Murata T. “A fuzzy classifier system that generates linguistic rules for pattern classification problems”, IEEE/Nagoya-University World Wisepersons Workshop, 35-54, 1995, https://doi.org/10.1007/3-540-61988-7\_15.
[15] Herrera F. “Genetic fuzzy systems: taxonomy, current research trends and prospects”, Evolutionary Intelligence, 1(1):27-46, 2008, https://doi.org/10.1007/s12065-007-0001-5.
[16] Castro P. A. D., Camargo H. D. A., “Improving the genetic optimization of fuzzy rule base by imposing a constraint condition on the number of rules”, congresso da sociedade brasileira de computacao December 2004.
[17] https://www.kaggle.com/datasets
[18] https://archive.ics.uci.edu/ml/datasets.php
[19] Tan C. H., Yap K. S., Yap H. J., “Application of genetic algorithm for fuzzy rules optimization on semi expert judgment automation using Pittsburg approach”, Applied Soft Computing 12(8): 2168–2177, 2012, https://doi.org/10.1016/j.asoc.2012.03.018.
[20] Soui M., Gasmi I., Smiti S., Ghédira K., “Rule-based credit risk assessment model using multi-objective evolutionary algorithms”, Expert Systems With Applications 126:144–157, 2019, https://doi.org/10.1016/j.eswa.2019.01.078.
[21] Han J., Kamber M., Pei J., “Data Mining Concepts and Techniques”, Third Edition, the Morgan Kaufmann Series in DataManagement Systems, 2011.
[22] Kulluk S., Özbakır L., Baykasoğlu A., “Fuzzy difaconn-miner: a novel approach for fuzzy rule extraction from neural networks” Expert Syst. Appl., 40(3): 938-946, 2013, https://doi.org/10.1016/j.eswa.2012.05.050.
[23] Ganapathy S., Sethukkarasi R., Yogesh P., Vijayakumar P., Kannan A., ”An intelligent temporal pattern classification system using fuzzy temporal rules and particle swarm optimization”, Sadhana, 39(2): 283-302, 2014, https://doi.org/10.1007/s12046-014-0236-7.
[24] Koshiyama A.S., Vellasco M.M.B.R., Tanscheit R., “GPFIS-class. A genetic fuzzy system based on genetic programming for classification problems”, Appl. Soft Comput., 37:561-571, 2015, https://doi.org/10.1016/j.asoc.2015.08.055.
[25] Zhu L., Qiu D., Ergu D., Ying C., Liu K., “A study on predicting loan default based on the random forest algorithm”, 7th International Conference on Information Technology and Quantitative Management, ITQM 2019, https://doi.org/10.1016/j.procs.2019.12.017.
[26] Safari A., Mazinani M., Hossein R., “A Novel Type-2 Adaptive Neuro Fuzzy Inference System Classifier for Modelling Uncertainty in Prediction of Air Pollution Disaster“, IJE 30(11):1746-1751, 2017.
[27] Hosseini R., Qanadli S. D., Barman S., Mazinani M., Ellis T., Dehmeshki J., “An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System”, IEEE Transactions on Fuzzy Systems 27(8), 2019, https://doi.org/10.1109/TFUZZ.2019.2921503.
[28] آخوندی ر.، حسینی ر.، «ارایه مدل هوشمند هایبریدی فازی-تکامل ژنتیکی تفاضلی در یک سیستم خبرة فازی برای پیش بینی خطر ابتلا به بیماری قلبی»، مجله محاسبات نرم، جلد 6، شماره 2، ص 32-47، 1396.
[29] قاسم‌احمد ل.، «مروری بر 7 الگوریتم برتر داده کاوی در پیش‌بینی مبتلایان به سرطان»، فصلنامه بیماری‌های پستان ایران، سال ششم، شماره اول، بهار 1392.
[30] لنگری‌زاده م.، مقبلی ف.، «مرور نظام‌مند کاربرد شبکه بیزین ساده در پیش‌بینی بیماری‌ها»، مجله انفورماتیک سلامت و زیست پزشکی، دوره سوم، شماره چهارم، 1395.
[31] مرادی فراهانی ح.، عسگری ج.، ذکری م.، «مروری بر منطق فازی نوع-2: از پیدایش تا کاربرد»، مجله محاسبات نرم، جلد ۲، شماره ۱، ص 22-43، 1392.
[32] قاسمی ر.، محمدی ح.، طاهر س.ع.، «کنترل فرکانس یک زیرشبکه جزیره‌ای با استفاده از کنترل هوشمند پاسخ‌گویی بار مبتنی بر منطق فازی و الگوریتم بهینه سازی ازدحام ذرات»، مجله محاسبات نرم، جلد 6، شماره 2، ص 18-31، 1396.
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