Design of Fuzzy Rule-based Classifiers through Granulation and Consolidation

[1] J. Abonyi, J. A. Roubos, and F. Szeifert, Data-driven generation of compact, accurate, and linguistically sound fuzzy classifiers based on a decision-tree initialization, International Journal of Approximate Reasoning, 23:1–21, 200310.1016/S0888-613X(02)00076-2Search in Google Scholar

[2] S. Aeberhard, D. Coomans, and O. de Vel, Comparative analysis of statistical pattern recognition methods in high dimensional settings, Pattern Recognition, 27(8):1065–1077, 199410.1016/0031-3203(94)90145-7Search in Google Scholar

[3] C. C. Aggarwal, Data Classification: Algorithms and Applications, Chapman & Hall/CRC, 1st edition, 2014Search in Google Scholar

[4] J. Alcala-Fdez, R. Alcala, and F. Herrera, A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning, IEEE Transactions on Fuzzy Systems, 19(5):857–872, 201110.1109/TFUZZ.2011.2147794Search in Google Scholar

[5] K. Bache and M. Lichman, UCI machine learning repository, http://archive.ics.uci.edu/ml, 2013Search in Google Scholar

[6] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and regression trees, Wadsworth & Brooks/Cole Advanced Books & Software, Monterey, 1984Search in Google Scholar

[7] B.-C. Chien, J.-Y. Lin, and W.-P. Yang, A classification tree based on discriminant functions, Journal of Information Science and Engineering, 222(3):573–594, 2006Search in Google Scholar

[8] S.-B. Cho, Neural-network classifiers for recognizing totally unconstrained handwritten numerals, IEEE Transactions on Neural Networks, 8(1):43–53, 199710.1109/72.554190Search in Google Scholar

[9] W. W. Cohen, Fast effective rule induction, In Proceedings of the Twelfth International Conference on Machine Learning, pages 115–123, Morgan Kaufmann, 199510.1016/B978-1-55860-377-6.50023-2Search in Google Scholar

[10] C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, 20(3):273–297, September 199510.1007/BF00994018Search in Google Scholar

[11] T. Cover and P. Hart, Nearest neighbor pattern classification, IEEE Transactions on Information Theory, 13(1):21–27, September 200610.1109/TIT.1967.1053964Search in Google Scholar

[12] R. Duda and P. Hart, Pattern Classification and Scene Analysis, Wiley, New York, 1973Search in Google Scholar

[13] I. W. Evett and E. J. Spiehler, Rule induction in forensic science, Technical report, Central Research Establishment, Home Office Forensic Science Service, 1987Search in Google Scholar

[14] R. A. Fisher, The use of multiple measurements in taxonomic problems, Annals of Eugenics, 7(2):179–188, 193610.1111/j.1469-1809.1936.tb02137.xSearch in Google Scholar

[15] J. H. Gennari, P. Langley, and D. Fisher, Models of incremental concept formation, Artificial Intelligence, 40(1–3):11–61, 198910.1016/0004-3702(89)90046-5Search in Google Scholar

[16] S. Guillaume and B. Charnomordic, Learning interpretable fuzzy inference systems with FisPro, Information Sciences, 180(20):4409–4427, 201110.1016/j.ins.2011.03.025Search in Google Scholar

[17] J. Hühn and E. Hüllermeier, FURIA: an algorithm for unordered fuzzy rule induction, Data Mining and Knowledge Discovery, 19(3):293–319, 200910.1007/s10618-009-0131-8Search in Google Scholar

[18] H. Ishibuchi, T. Nakashima, and T. Murata, Three-objective genetic-based machine learning for linguistic rule extraction, Information Sciences, 136(1–4):109–133, 200110.1016/S0020-0255(01)00144-XSearch in Google Scholar

[19] H. Ishibuchi, K. Nozaki, N. Yamamoto, and H. Tanaka, Selecting fuzzy if-then rules for classification problems using genetic algorithms, IEEE Transactions on Fuzzy Systems, 3(3):260–270, 199510.1109/91.413232Search in Google Scholar

[20] H. Ishibuchi and T. Yamamoto, Rule weight specification in fuzzy rule-based classification systems, IEEE Transactions on Fuzzy Systems, 13(4):428–435, 200510.1109/TFUZZ.2004.841738Search in Google Scholar

[21] L. Kuncheva, Fuzzy Classifier Design, Springer-Verlag, Heidelberg, 200010.1007/978-3-7908-1850-5Search in Google Scholar

[22] K. Larsen, Generalized naive bayes classifiers, SIGKDD Explorations, 7(1):76–81, 200510.1145/1089815.1089826Search in Google Scholar

[23] R. P. Lippmann, Neural network classifiers for speech recognition, The Lincoln Laboratory Journal, 1:107–124, 1988Search in Google Scholar

[24] C. Mencar, C. Castiello, R. Cannone, and A. M. Fanelli, Interpretability assessment of fuzzy knowledge bases: A cointension based approach, International Journal of Approximate Reasoning, 52(4):501–518, 201110.1016/j.ijar.2010.11.007Search in Google Scholar

[25] J. R. Quinlan, Induction of decision trees, Machine Learning, 1(1):81–106, 198610.1007/BF00116251Search in Google Scholar

[26] J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993Search in Google Scholar

[27] Y. Ren, L. Zhang, and P. N. Suganthan, Ensemble classification and regression - recent developments, applications and future directions, IEEE Computational Intelligence Magazine, 11(1):41–53, 201610.1109/MCI.2015.2471235Search in Google Scholar

[28] A. Riid and J.-S. Preden, Interpretability improvement of fuzzy rule-based classifiers via rule compression, In Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology, pages 162–169, Gijon, Spain, 201510.2991/ifsa-eusflat-15.2015.26Search in Google Scholar

[29] A. Riid and M. Sarv, Determination of regional variants in the versification of estonian folksongs using an interpretable fuzzy rule-based classifier, In Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2013), pages 61–66, Milan, Italy, 201310.2991/eusflat.2013.9Search in Google Scholar

[30] L. Rokach, Pattern Classification Using Ensemble Methods, volume 75 of Series in Machine Perception and Artifical Intelligence, World Scientific Publishing Company, Singapore, 201010.1142/7238Search in Google Scholar

[31] H. Roubos, M. Setnes, and J. Abonyi, Learning fuzzy classification rules from data, Information Sciences, 150(1–2):77–93, 200310.1016/S0020-0255(02)00369-9Search in Google Scholar

[32] J. W. Smith, J. E. Everhart, W. C. Dickson, W. C. Knowler, and R. S. Johannes, Using the ADAP learning algorithm to forecast the onset of diabetes mellitus, In Proceedings of the Symposium on Computer Applications and Medical Care, pages 261–265, Los Alamitos, CA, 1988Search in Google Scholar

[33] W. N. Street, W. H. Wolberg, and O. L. Mangasarian, Nuclear feature extraction for breast tumor diagnosis, In Proceedings of the IS&T 1993 International Symposium on Electronic Imaging: Science and Technology, volume 1905, pages 861–870, San Jose, CA, 199310.1117/12.148698Search in Google Scholar

[34] P. D. Turney, Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm, Journal of Artificial Intelligence Research, 2:369–409, 199510.1613/jair.120Search in Google Scholar

[35] W. H Wolberg and O. L. Mangasarian, Multisurface method of pattern separation for medical diagnosis applied to breast cytology, Proceedings of the National Academy of Sciences, 87:9193–9196, 199010.1073/pnas.87.23.9193551302251264Search in Google Scholar