Designing rule-based fuzzy systems for classification in medicine

Knowledge-Based Systems

Volumn 124, Issue , 2017, Pages 105-132

  Pota, Marco ^a   Esposito, Massimo ^a   De Pietro, Giuseppe ^a  

^a CNR   (Italy)

Author keywords

Classification; Fuzzy models; Learning; Medicine; Rule based classifiers

Indexed keywords

ARTIFICIAL INTELLIGENCE; BENCHMARKING; DECISION SUPPORT SYSTEMS; DEGREES OF FREEDOM (MECHANICS); ECONOMIC AND SOCIAL EFFECTS; FUZZY INFERENCE; FUZZY LOGIC; FUZZY SETS; FUZZY SYSTEMS; MECHANICS; MEDICINE;

CLASSIFICATION METHODS; DECISION SUPPORT SYSTEM (DSSS); FUZZY MODELS; KNOWLEDGE EXTRACTION AND REPRESENTATIONS; LEARNING; RULE-BASED CLASSIFIER; SYSTEM CHARACTERISTICS; WISCONSIN BREAST CANCER DATASET;

CLASSIFICATION (OF INFORMATION);

EID: 85017150802     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2017.03.006     Document Type: Article

References (71)

1. [1] Ortiz, A., Munilla, J., Górriz, J.M., Ramírez, J., Ensembles of deep learning architectures for the early diagnosis of the Alzheimer's disease. Int. J. Neural Syst., 26(7), 2016, 1650025.
2. [2] Zadeh, L., Fuzzy sets. Inf. Control 8 (1965), 338–353.
3. [3] J.Gacto, M., Alcalá, R., Herrera, F., Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures. Inf. Sci. 181 (2011), 4340–4360.
4. [4] Mencar, C., Interpretability of fuzzy systems. Fuzzy Logic and Applications, 8256, 2013, Springer, Cham, 22–35.
5. [5] Gupta, N., Jain, S.K., Comparative analysis of fuzzy power system stabilizer using different membership functions. Int. J. Comput. Electr. Eng. 2:2 (2010), 262–267.
6. [6] Monicka, J.G., Sekhar, N., Kumar, K.R., Performance evaluation of membership functions on fuzzy logic controlled AC voltage controller for speed control of induction motor drive. Int. J. Comput. Appl. 13:5 (2011), 8–12.
7. [7] Zhao, J., Bose, B., Evaluation of membership functions for fuzzy logic controlled induction motor drive. Proceedings of the Twenty-Eighth IEEE Annual Conference of the Industrial Electronics Society, Sevilla, Spain, 2002, 229–234.
8. [8] Rutkowski, L., Cpalka, K., Flexible neuro-fuzzy systems. IEEE Trans. Neural Netw. 14:3 (2003), 554–574.
9. [9] Mencar, C., Fanelli, A.M., Interpretability constraints for fuzzy information granulation. Inf. Sci. 178:24 (2008), 4585–4618.
10. [10] Alcalà, R., Gacto, M.J., Herrera, F., Alcalà-Fdez, J., A multi-objective genetic algorithm for tuning and rule selection to obtain accurate and compact linguistic fuzzy rule-based systems. Int. J. Uncertain. Fuzziness Knowl. Based Syst., 15(05), 2007, 539.
11. [11] Alcalà, R., Ducange, P., Herrera, F., Lazzerini, B., Marcelloni, F., A multiobjective evolutionary approach to concurrently learn rule and data bases of linguistic fuzzy-rule-based systems. IEEE Trans. Fuzzy Syst. 17:5 (2009), 1106–1122.
12. [12] Márquez, A.A., Márquez, F.A., Peregrín, A., A multi-objective evolutionary algorithm with an interpretability improvement mechanism for linguistic fuzzy systems with adaptive defuzzification. Proceedings of the IEEE International Conference on Fuzzy Systems, 2010, 18–23.
13. [13] Guillaume, S., Charnomordic, B., Learning interpretable fuzzy inference systems with FisPro. Inf. Sci. 181 (2011), 4409–4427.
14. [14] Antonelli, M., Ducange, P., Lazzerini, B., Marcelloni, F., Learning concurrently data and rule bases of Mamdani fuzzy rule-based systems by exploiting a novel interpretability index. Soft Comput. 15:10 (2011), 1981–1998.
15. [15] Gorzałczany, M.B., Rudziński, F., Accuracy vs. interpretability of fuzzy rule-based classifiers: an evolutionary approach. Lect. Notes Comput. Sci. 7269 (2012), 222–230.
16. [16] Cano, A., Zafra, A., Ventura, S., An interpretable classification rule mining algorithm. Inf. Sci. 240 (2013), 1–20.
17. [17] Rudzínski, F., A multi-objective genetic optimization of interpretability-oriented fuzzy rule-based classifiers. Appl. Soft Comput. 38 (2016), 118–133.
18. [18] Pota, M., Esposito, M., Degrees of Freedom and Advantages of Different Rule-Based Fuzzy Systems. Mastrorakis, N.E., Pardalos, P.M., Agarwal, R.P., Kočinac, L., (eds.) Advances in Applied and Pure Mathematics−Proceedings of the 2014 International Conference on Pure Mathematics, Applied Mathematics and Computational Methods (PMAMCM 2014), Santorini Island, Greece, July 17-21, 2014, Mathematics and Computers in Science and Engineering Series, Vol. 29, 2014, 107–114 ISSN:2227-4588.
19. [19] Pota, M., Esposito, M., Insights into interpretability of neuro-fuzzy systems. Proceedings of the 16th World Congress of the International Fuzzy Systems Association (IFSA) and the 9th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), Gijòn, 2015, Atlantis Press, Spain, 1427–1434, 10.2991/ifsa-eusflat-15.2015.202.
20. [20] Bache, K., Lichman, M., UCI Machine Learning Repository. 2013, School of Information and Computer Science, University of California, Irvine, CA.
21. [21] Mann, H.B., Whitney, D.R., On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18:1 (1947), 50–60.
22. [22] De la Hoz, E., De La Hoz, E., Ortiz, A., Ortega, J., Prieto, B., PCA filtering and probabilistic SOM for network intrusion detection. Neurocomputing 164 (2015), 71–81.
23. [23] Pearson, K., Notes on regression and inheritance in the case of two parents. Proc. R. Soc. Lond. 58 (1895), 240–242.
24. [24] Pota, M., Scalco, E., Sanguineti, G., Cattaneo, G.M., Esposito, M., Rizzo, G., Early classification of parotid glands shrinkage in radiotherapy patients: a comparative study. Biosyst. Eng. 138 (2014), 77–89.
25. [25] Wu, D., Twelve considerations in choosing between Gaussian and trapezoidal membership functions in interval type-2 fuzzy logic controllers. Proceedings of the IEEE World Congress on Computational Intelligence, Brisbane, Australia, 2012, 1–8.
26. [26] Barrile, V., Bilotta, G., Object-oriented analysis applied to high resolution satellite data. WSEAS Trans. Signal Process. 4:3 (2008), 68–75.
27. [27] Rothenfluh, T.E., Bögl, K., Adlassnig, K.-P., Representation and acquisition of knowledge for a fuzzy medical consultation system. Szczepaniak, P.S., Lisboa, P.J.G., Kacprzyk, J., (eds.) Fuzzy systems in Medicine Studies in Fuzziness and Soft Computing, 41, 2000, Physica-Verlag HD, 636–651.
28. [28] Alonso, J.M., Castiello, C., Lucarelli, M., Mencar, C., Modelling interpretable fuzzy rule-based classifiers for medical decision support. Magdalena, R., Soria, E., Guerrero, J., Gómez-Sanchis, J., Serrano, A.J., (eds.) Medical Applications of Intelligent Data Analysis: Research Advancements, 2012, IGI Global, 255–272.
29. [29] Guillaume, S., Designing fuzzy inference systems from data: an interpretability-oriented review. IEEE Trans. Fuzzy Syst. 9 (2001), 426–443.
30. [30] Quinlan, J.R., Induction on decision trees. Mach. Learn. 1 (1986), 81–106.
31. [31] MacQueen, J.B., Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Symposium on Mathematical Statistics and Probability, Berkeley, 2001, 281–297.
32. [32] C.F. Eick, N. Zeidat, Z. Zhao (2005). Supervised clustering – algorithms and benefits, Technical Report Number UH-CS-05–10, IEEE.
33. [33] Bezdek, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms. 1981, Kluwer Academic Publishers, NorwellMAUSA.
34. [34] Pedrycz, W., Fuzzy equalization in the construction of fuzzy sets. Fuzzy Sets Syst. 64 (1994), 21–30.
35. [35] Yuan, Y., Shaw, M.J., Induction of fuzzy decision trees. Fuzzy Sets Syst. 65 (1995), 125–139.
36. [36] Abonyi, J., Szeifert, F., Supervised fuzzy clustering for the identification of fuzzy classifiers. Pattern Recognit. Lett. 24 (2003), 2195–2207.
37. [37] Pota, M., Esposito, M., De Pietro, G., Hybridization of possibility theory and supervised clustering to build DSSs for classification in medicine. Proceedings of the Twelvth International Conference on Hybrid Intelligent Systems (HIS), Pune, 2012, IEEE, India, 490–495.
38. [38] Esposito, M., De Falco, I., De Pietro, G., An evolutionary-fuzzy DSS for assessing health status in multiple sclerosis disease. Int. J. Med. Inf. 80:12 (2011), e245–e254.
39. [39] Pena-Rees, C.A., Sipper, M., A fuzzy-genetic approach to breast cancer diagnosis. Artif. Intell. Med. 17 (1999), 131–155.
40. [40] Huang, M.-J., Tsou, Y.-L., Lee, S.-C., Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge. Knowl. Based Syst. 19:6 (2006), 396–403.
41. [41] Pota, M., Esposito, M., De Pietro, G., Transforming probability distributions into membership functions of fuzzy classes: a hypothesis test approach. Fuzzy Sets Syst. 233 (2013), 52–73.
42. [42] Pota, M., Esposito, M., De Pietro, G., Approximation of statistical information with fuzzy models for classification in medicine. Skulimowski, A.M.J., Kacprzyk, J., (eds.) Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions Advances in Intelligent Systems and Computing, 2016, Springer International Publishing, 359–371 364.
43. [43] Pota, M., Esposito, M., De Pietro, G., Fuzzy partitioning for clinical DSSs using statistical information transformed into possibility-based knowledge. Knowl. Based Syst. 67 (2014), 1–15.
44. [44] Glorennec, P.Y., Algorithmes D'apprentissage Pour Systèmes D'inférence Floue. 1999, Hermes Science Publications, Paris.
45. [45] Wang, L.X., Mendel, J.M., Generating fuzzy rules by learning from examples. IEEE. Trans. Syst. Man. Cybern. 22 (1992), 1414–1427.
46. [46] Mamdani, E.H., Assilian, S., An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7:1 (1975), 1–13.
47. [47] Sugeno, M., Industrial Applications of Fuzzy Control. 1985, Elsevier Science Pub. Co.
48. [48] Babuska, R., Verbruggen, H.B., An overview of fuzzy modelling for control. Control Eng. Pract. 4:11 (1996), 1593–1606.
49. [49] Guillaume, S., Charnomordic, B., A new method for inducing a set of interpretable fuzzy partitions and fuzzy inference from data. Stud. Fuzziness Soft Comput. 128 (2003), 148–175.
50. [50] Yager, R.R., Filev, D.P., Essentials of Fuzzy Modeling and Control. 1994, Wiley, New York.
51. [51] Van Leekwijck, W., Kerre, E.E., Defuzzification: criteria and classification. Fuzzy Sets Syst. 108 (1999), 159–178.
52. [52] Ghazavi, S.N., Liao, T.W., Medical data mining by fuzzy modeling with selected features. Artif. Intell. Med. 43 (2008), 195–206.
53. [53] Mencar, C., Fanelli, A., Interpretability constraints for fuzzy information granulation. Inf. Sci. 178 (2008), 4585–4618.
54. [54] Pulkkinen, P., Koivisto, H., A dynamically constrained multiobjective genetic fuzzy system for regression problems. IEEE Trans. Fuzzy Syst. 18 (2010), 61–177.
55. [55] Fazendeiro, P., de Oliveira, J.V., Pedrycz, W., A multiobjective design of a patient and anaesthetist-friendly neuromuscular blockade controller. IEEE. Trans. Biomed. Eng. 54 (2007), 1667–1678.
56. [56] Mikut, R., Jakel, J., Grall, L., Interpretability issues in data-based learning of fuzzy systems. Fuzzy Sets Syst. 150 (2005), 179–197.
57. [57] Marquez, A., Márquez, F., Peregrin, A., A multi-objective evolutionary algorithm with an interpretability improvement mechanism for linguistic fuzzy systems with adaptive defuzzification. Proceedings of the IEEE World Congress on Computational Intelligence, 2010.
58. [58] Castellano, G., Fanelli, A.M., Mencar, C., A neuro-fuzzy network to generate human-understandable knowledge from data. Cognit. Syst. Res. 3 (2002), 125–144.
59. [59] Wolfram Research, Inc., Mathematica, Version 8.0, Wolfram Research, Inc., Champaign, IL (2010).
60. [60] Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H., The WEKA data mining software: an update. SIGKDD Explor. 11:1 (2009), 10–18.
61. [61] Subramanian, D.K., Ananthanarayana, V.S., Narasimha Murty, M., Knowledge-based association rule mining using and-or taxonomies. Knowl. Based Syst. 16:1 (2003), 37–45.
62. [62] Quinlan, J.R., Improved use of continuous attributes in C4.5. J. Artif. Intell. Res. 4 (1996), 77–90.
63. [63] Cooper, G., Herskovits, E., A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9:4 (1992), 309–347.
64. [64] Dutton, D.M., Conroy, G.V., A review of machine learning. Knowl. Eng. Rev. 12:4 (1996), 341–367.
65. [65] Platt, J., Fast training of support vector machines using sequential minimal optimization. Schoelkopf, B., Burges, C., Smola, A., (eds.) Advances in Kernel Methods – Support Vector Learning, 1998, MIT Press Cambridge, MA, USA.
66. [66] O'Connor, N., Madden, M.G., A neural network approach to predicting stock exchange movements using external factors. Knowl. Based Syst. 19:5 (2006), 371–378.
67. [67] Friedman, M., The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Amer. Statist. Assoc. 32 (1937), 675–701.
68. [68] Friedman, M., A comparison of alternative tests of significance for the problem of m rankings. Ann. Math. Stat. 11 (1940), 86–92.
69. [69] Nemenyi, P.B., Distribution-free multiple comparisons. (Ph.D. thesis)., 1963, Princeton University.
70. [70] Dunn, O.J., Multiple comparisons among means. J. Amer. Statist. Assoc. 56 (1961), 52–64.
71. [71] Holm, S., A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6 (1979), 65–70.