Design of fuzzy rule-based classifiers through granulation and consolidation

• Autorzy: Riid A. , Preden J.-S.

Identyfikatory

DOI

10.1515/jaiscr-2017-0010

• Języki publikacji: EN

Abstrakty

EN

This paper addresses the issue how to strike a good balance between accuracy and compactness in classification systems - still an important question in machine learning and data mining. The fuzzy rule-based classification approach proposed in current paper exploits the method of rule granulation for error reduction and the method of rule consolidation for complexity reduction. The cooperative nature of those methods - the rules are split in a way that makes efficient rule consolidation feasible and rule consolidation itself is capable of further error reduction - is demonstrated in a number of experiments with nine benchmark classification problems. Further complexity reduction, if necessary, is provided by rule compression.

Słowa kluczowe

EN

pattern recognition; fuzzy classification; complexity reduction

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2017
• Tom: Vol. 7, No. 2
• Strony: 137--147
• Opis fizyczny: Bibliogr. 35 poz., rys.

Twórcy

autor

Riid A.

• Laboratory for Proactive Technologies, Tallinn University of Technology, Ehitajate tee 5, 19086, Tallinn, Estonia

autor

Preden J.-S.

• Laboratory for Proactive Technologies, Tallinn University of Technology, Ehitajate tee 5, 19086, Tallinn, Estonia

Bibliografia

• [1] J. Abonyi, J. A. Roubos, and F. Szeifert, Datadriven generation of compact, accurate, and linguistically sound fuzzy classifiers based on a decisiontree initialization, International Journal of Approximate Reasoning, 23:1–21, 2003
• [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, 1994
• [3] C. C. Aggarwal, Data Classification: Algorithms and Applications, Chapman & Hall/CRC, 1st edition, 2014
• [4] J. Alcala-Fdez, R. Alcala, and F. Herrera, A fuzzy association rule-based classification model for highdimensional problems with genetic rule selection and lateral tuning, IEEE Transactions on Fuzzy Systems, 19(5):857–872, 2011
• [5] K. Bache and M. Lichman, UCI machine learning repository, http://archive.ics.uci.edu/ml, 2013
• [6] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and regression trees, Wadsworth & Brooks/Cole Advanced Books & Software, Monterey, 1984
• [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, 2006
• [8] S.-B. Cho, Neural-network classifiers for recognizing totally unconstrained handwritten numerals, IEEE Transactions on Neural Networks, 8(1):43–53, 1997
• [9] W. W. Cohen, Fast effective rule induction, In Proceedings of the Twelfth International Conference on Machine Learning, pages 115–123, Morgan Kaufmann, 1995
• [10] C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, 20(3):273–297, September 1995
• [11] T. Cover and P. Hart, Nearest neighbor pattern classification, IEEE Transactions on Information Theory, 13(1):21–27, September 2006
• [12] R. Duda and P. Hart, Pattern Classification and Scene Analysis, Wiley, New York, 1973
• [13] I. W. Evett and E. J. Spiehler, Rule induction in forensic science, Technical report, Central Research Establishment, Home Office Forensic Science Service, 1987
• [14] R. A. Fisher, The use of multiple measurements in taxonomic problems, Annals of Eugenics, 7(2):179–188, 1936
• [15] J. H. Gennari, P. Langley, and D. Fisher, Models of incremental concept formation, Artificial Intelligence, 40(1–3):11–61, 1989
• [16] S. Guillaume and B. Charnomordic, Learning interpretable fuzzy inference systems with FisPro, Information Sciences, 180(20):4409–4427, 2011
• [17] J. Huhn and E. H ¨ ullermeier, FURIA: an algorithm ¨ for unordered fuzzy rule induction, Data Mining and Knowledge Discovery, 19(3):293–319, 2009
• [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, 2001
• [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, 1995
• [20] H. Ishibuchi and T. Yamamoto, Rule weight specification in fuzzy rule-based classification systems, IEEE Transactions on Fuzzy Systems, 13(4):428–435, 2005
• [21] L. Kuncheva, Fuzzy Classifier Design, SpringerVerlag, Heidelberg, 2000
• [22] K. Larsen, Generalized naive bayes classifiers, SIGKDD Explorations, 7(1):76–81, 2005
• [23] R. P. Lippmann, Neural network classifiers for speech recognition, The Lincoln Laboratory Journal, 1:107–124, 1988
• [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, 2011
• [25] J. R. Quinlan, Induction of decision trees, Machine Learning, 1(1):81–106, 1986
• [26] J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993
• [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, 2016
• [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, 2015
• [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, 2013
• [30] L. Rokach, Pattern Classification Using Ensemble Methods, volume 75 of Series in Machine Perception and Artifical Intelligence, World Scientific Publishing Company, Singapore, 2010
• [31] H. Roubos, M. Setnes, and J. Abonyi, Learning fuzzy classification rules from data, Information Sciences, 150(1–2):77–93, 2003
• [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, 1988
• [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: Scienceand Technology, volume 1905, pages 861–870, San Jose, CA, 1993
• [34] P. D. Turney, Cost-sensitive classification: Empiricalevaluation of a hybrid genetic decision tree induction algorithm, Journal of Artificial Intelligence Research, 2:369–409, 1995
• [35] W. H Wolberg and O. L. Mangasarian, Multisurface method of pattern separation for medical diagnosis applied to breast cytology, Proceedings ofthe National Academy of Sciences, 87:9193–9196, 1990

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).