[1] بزرگ زاده لیاقت, بختیاری مرتضی, شهنی کرم زاده نیما, اسماعیل دوست محمد. تخمین ارتفاع امواج ناشی از باد با استفاده از سیستم استنتاج تطبیقی عصبی- فازی، درخت تصمیم و روشهای تجربی در بندر بوشهر. (1397). علوم مهندسی و آبیاری (مجله علوم کشاورزی). دوره 41 , شماره 2، 166-151.
[2] جعفری، بهزاد، عادل آذز. درخت تصمیم فازی؛ رویکردی نوین در تدوین استراتژی. (1392). پژوهش‌های مدیریت عمومی. سال ششم شماره 19، 39-25.
[3] ضایی اینانلو, سلیمانیان قره چپق, و مجاهدی. (1397). ارائه مدلی بر مبنای درخت تصمیم فازی جهت تشخیص مزاج در طب سنتی ایرانی. مجله طب سنتی اسلام و ایران, 9(3), 227-237.
[4] طاهری، محمود، ماشین چی، ماشالله. مقدمه‌ای بر احتمال و آمار فازی. (1392). انتشارات دانشگاه شهید باهنر کرمان.
[5] Cintra, M. E., Monard, M. C., & Camargo, H. A. (2013). A fuzzy algorithm based on c4. 5. Hot Topics.
[6] Esposito, F., Malerba, D., Semeraro, G., & Kay, J. (1997). A comparative analysis of methods for pruning decision trees. IEEE transactions on pattern analysis and machine intelligence, 19(5), 476-491.
[7] Gupta, A., Mehrotra, K. G., & Mohan, C. (2010). A clustering-based discretization for supervised learning. Statistics & probability letters, 80(9-10), 816-824.
[8] Janikow, C. Z. (1998). Fuzzy decision trees: issues and methods. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 28(1), 1-14.
[9] Jensen, R., & Shen, Q. (2005). Fuzzy-rough feature significance for fuzzy decision trees. In Proc. 2005 UK Workshop on Computational Intelligence, 89-96.
[10] Kotsiantis, S., & Kanellopoulos, D. (2006). Discretization techniques: A recent survey. GESTS International Transactions on Computer Science and Engineering, 32(1), 47-58.
[11] Kuncheva, L. I. (2014). Combining pattern classifiers: methods and algorithms. John Wiley & Sons.
[12] Lee, K. M., Lee, K. M., Lee, J. H., & Lee-Kwang, H. (1999). A fuzzy decision tree induction method for fuzzy data, in Proc. of 8th IEEE Inter. conference on Fuzzy Systems, 16 -21.
[13] Levashenko, V., & Zaitseva, E. (2012). Fuzzy decision trees in medical decision making support system. In 2012 Federated Conference on Computer Science and Information Systems (FedCSIS) (pp. 213-219). IEEE.
[14] Mehta, M., Rissanen, J., & Agrawal, R. (1995). MDL-Based Decision Tree Pruning. In KDD, 21(2), 216-221.
[15] Mingers, J. (1989). An empirical comparison of pruning methods for decision tree induction. Machine learning, 4(2), 227-243.
[16] Niblett, T., & Bratko, I. (1987). Learning decision rules in noisy domains. In Proceedings of Expert Systems’ 6, the 6th Annual Technical Conference on Research and development in expert systems III, 25-34.
[17] Olaru, C.,& Wehenkel, L. (2003). A complete fuzzy decision tree technique. Fuzzy sets and systems, 138(2), 221-254.
[18] Pedrycz, W., & Sosnowski, Z. A. (2005). C-fuzzy decision trees. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 35(4), 498-511.
[19] Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1), 81-106.
[20] Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.
[21] Umano,M. Okamoto,H. Hatono,I. Tamura,H. Kawachi, F. Umedzu,S. Kinoshita,J. Fuzzy Decision Trees by Fuzzy ID3 algorithm and Its Application to Diagnosis Systems. In Proceedings of the Third IEEE Conference on Fuzzy Systems, vol. 3, pp. 2113-2118, Orlando, June, 1994.
[22] Wang, X., & Borgelt, C. (2004). Information measures in fuzzy decision trees. In 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No. 04CH37542), 85-90.
[23] Wang, X., Chen, B., Qian, G., & Ye, F. (2000). On the optimization of fuzzy decision trees. Fuzzy Sets and Systems, 112(1), 117-125.
[24] Wang, X., Liu, X., Pedrycz, W., & Zhang, L. (2015). Fuzzy rule based decision trees. Pattern Recognition, 48(1), 50-59.
[25] Xizhao, W., & Hong, J. (1998). On the handling of fuzziness for continuousvalued attributes in decision tree generation. Fuzzy Sets and Systems, 99(3), 283- 290.
[26] Yuan, Y., & Shaw, M. J. (1995). Induction of fuzzy decision trees. Fuzzy Sets and systems, 69(2), 125-139.
[27] Rabcan, J., Levashenko, V., Zaitseva, E., & Kvassay, M. (2020). Review of methods for EEG signal classification and development of new fuzzy classificationbased approach. IEEE Access, 8, 189720-189734.
[28] Rabcan, J., Levashenko, V., Zaitseva, E., Kvassay, M., & Subbotin, S. (2019). Application of fuzzy decision tree for signal classification. IEEE Transactions on Industrial Informatics, 15(10), 5425-5434.
[29] Idris, N. F., & Ismail, M. A. (2021). Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition. PeerJ Computer Science, 7, e427.
[30] Liang, G., van den Berg, J., & Kaymak, U. (2005). A comparative study of three Decision Tree algorithms: ID3, Fuzzy ID3 and Probabilistic Fuzzy ID3. Netherlands: Erasmus University.
[31] Ludwig, S. A., Picek, S., & Jakobovic, D. (2018). Classification of cancer data: analyzing gene expression data using a fuzzy decision tree algorithm. In Operations research applications in health care management (pp. 327-347). Springer, Cham.
[32] Zadeh, L. A. (1965). Fuzzy Sets, Inform. Control, 8: 338-353.
[33] Zeinalkhani, M., & Eftekhari, M. (2014). Fuzzy partitioning of continuous attributes through discretization methods to construct fuzzy decision tree classifiers. Information Sciences, 278, 715-735.
)