Selection of heterogeneous fuzzy model ensembles using self-adaptive genetic algorithms

New Generation Computing

Volumn 29, Issue 3, 2011, Pages 309-327

  Smȩtek, Magdalena ^a   Trawiński, Bogdan ^a  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Ensemble selection; Fuzzy models; Heterogeneous ensem bles; Self adaptive GA

Indexed keywords

ENSEMBLE SELECTION; FUZZY MODELS; HETEROGENEOUS ENSEM- BLES; MODEL SELECTION; PREDICTIVE ACCURACY; REAL ESTATE; SELF ADAPTING; SELF ADAPTIVE ALGORITHMS; SELF ADAPTIVE GENETIC ALGORITHM; SELF-ADAPTIVE;

ADAPTIVE ALGORITHMS;

GENETIC ALGORITHMS;

EID: 80053928378     PISSN: 02883635     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00354-010-0305-3     Document Type: Article

References (63)

1. Alcalá, R., Alcalá-Fdez, J., Casillas, J., Cordón, O., Herrera, F., "Local iden tification of prototypes for genetic learning of accurate TSK fuzzy rule-based systems, " International Journal of Intelligent Systems, 22, 9, pp. 909-941, 2007.
2. Alcalá, R., Cordón, O., Herrera, F., "Combining Rule Weight Learning and Rule Selection to Obtain Simpler and More Accurate Linguistic Fuzzy Models, " Modeling with Words (Lawry, J. ed.), LNCS 2873, pp. 44-63, Springer, Heidelberg, 2003.
3. Alcalá-Fdez, J., Fernandez, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F., "KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework, " Journal of Multiple-Valued Logic and Soft Computing, in press, 2011.
4. Alcalá-Fdez, J., Sánchez, L, García, S, del Jesus, M. J., Ventura, S., Garrell, J. M., Otero, J., Romero, C., Bacardit, J., Rivas, V. M., Fernández, J. C., Herrera, F., "KEEL: A Software Tool to Assess Evolutionary Algorithms to Data Mining Problems, " Soft Computing, 13, 3, pp. 307-318, 2009.
5. Angeline, P. J., "Adaptive and self-adaptive evolutionary computations, " in Computational Intelligence: A Dynamic Systems Perspective (Palaniswami, M. and Attikiouzel, Y. eds.), pp. 152-163, IEEE Press, New York, 1995.
6. Bäck, T., Schwefel, H.-P., "An Overview of Evolutionary Algorithms for Parameter Optimization, " Evolutionary Computation, 1, 1, pp.1-23, 1993.
7. Bäck, T., "Self-adaptation in genetic algorithms, " in Toward a Practice of Autonomous Systems (Varela, F. J. and Bourgine, P. eds.), in Proc. First European Conference on Artificial Life, pp. 263-271, MIT Press, 1992.
8. Bańczyk, K., Kempa, O., Lasota, T., Trawiński, B., "Empirical Comparison of Bagging Ensembles Created Using Weak Learners for a Regression Problem, " ACIIDS 2011 (Nguyen, N. T., Kim, C.-G. and Janiak, A. eds.), LNAI 6592, pp. 312-322, Springer, Heidelberg, 2011.
9. Banfield, R. E., Hall, L. O., Bowyer, K. W., Kegelmeyer, W. P., "A comparison of decision tree ensemble creation techniques, " IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 1, pp. 173-180, 2007.
10. Biau, G., Cérou, F., Guyader, A., "On the Rate of Convergence of the Bagged Nearest Neighbor Estimate, " Journal of Machine Learning Research, 11, pp. 687-712, 2010.
11. Brown, G., Wyatt, J., Tinõ, P., "Managing Diversity in Regression Ensembles, " Journal of Machine Learning Research, 6, pp. 1621-1650, 2005.
12. Bühlmann, P., Yu, B., "Analyzing bagging, " Annals of Statistics, 30, pp. 927-961, 2002.
13. Carse, B., Fogarty, T. C., Munro, A., "Evolving fuzzy rule based controllers using genetic algorithms, " Fuzzy Sets and Systems, 80, 3, pp. 273-293, 1996.
14. Caruana, R., Niculescu-Mizil, A., Crew, G., Ksikes, A., "Ensemble selection from libraries of models, " in Proc. of the 21st International Conference on Machine Learning, ACM Press, pp. 137-144, 2004.
15. Casillas, J., Cordón, O., Herrera, F., "COR: A Methodology to Improve ad hoc Data-Driven Linguistic Rule Learning Methods by Inducing Cooperation Among Rules, " IEEE Trans. on System, Man and Cybernetics, Part B: Cybernetics, 32, 4, pp. 526{537, 2002.
16. Chandra, A., Yao, X., "Evolving hybrid ensembles of learning machines for better generalization, " Neurocomputing, 69, pp. 686-700, 2006.
17. Cordón, O., Herrera, F., "A three-stage evolutionary process for learning descriptive and approximate fuzzy logic controller knowledge bases from examples, " International Journal of Approximate Reasoning, 17, 4, pp. 369-407, 1997.
18. Cordón, O., Herrera, F., "Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems, " Fuzzy Sets and Systems, 118, 2, pp. 235-255, 2001.
19. Cordón, O., Quirin, A., "Comparing Two Genetic Overproduce-and-choose Strategies for Fuzzy Rule-based Multiclassification Systems Generated by Bagging and Mutual Information-based Feature Selection, " International Journal of Hybrid Intelligent Systems, 7, 1, pp. 45-64, 2010.
20. De Jong, K., "An analysis of the behavior of a class of genetic adaptive systems" Ph.D. thesis, University of Michigan, 1975.
21. Deb, K., Beyer, H.-G., "Self-adaptive genetic algorithms with simulated binary crossover, " Evolutionary Computation, 9, 2, pp. 197-221, 2001.
22. Digalakis, J. G., Margaritis, K. G., "An Experimental Study of Benchmarking Functions for Genetic Algorithms, " Int. J. Computer Math, 79, 4, pp. 403-416, 2002.
23. Dos Santos, E. M., Sabourin, R., Maupin, P., "A dynamic overproduce-and choose strategy for the selection of classifier ensembles, " Pattern Recognition, 41, pp. 2993-3009, 2008.
24. Dutta, H., "Measuring Diversity in Regression Ensembles, " in Proc. of the 4th Indian International Conference on Artificial Intelligence (Prasad, B., Lingras, P., Ram, A. eds.), IICAI 2009, Tumkur, Karnataka, India, pp. 2220-2236, 2009.
25. Eiben, E., Hinterding, R., Michalewicz, Z., "Parameter control in evolutionary algorithms, " IEEE Transactions on Evolutionary Computation, 3, 2, pp. 124-141, 1999.
26. García-Pedrajas, N., "Constructing Ensembles of Classifiers by Means of Weighted Instance Selection, " IEEE Transactions on Neural Networks, 20, 2, pp. 258-277, 2009.
27. Graczyk, M., Lasota, T., Trawiński, B., "Comparative Analysis of Premises Valuation Models Using KEEL, RapidMiner, and WEKA, " in ICCCI 2009, LNAI 5796 (Nguyen, N. T. et al. eds.), pp. 800-812, Springer, Heidelberg, 2009.
28. Graczyk, M., Lasota, T., Telec, Z., Trawiński, B., "A Multi-agent System to Assist with Property Valuation Using Heterogeneous Ensembles of Fuzzy Models, " in KES-AMSTA 2010, LNAI 6070 (Jedrzejowicz, P. et al. eds.), pp. 420-429, Springer, Heidelberg, 2010.
29. Graczyk, M., Lasota, T., Trawínski, B., Trawiński, K., "Comparison of Bagging, Boosting and Stacking Ensembles Applied to Real Estate Appraisal, " in ACIIDS 2010, LNAI 5991 (Nguyen, N. T. et al. eds.), pp. 340-350, Springer, Heidelberg, 2010.
30. Hansen, N., Ostermeier, A., "Completely derandomized self-adaptation in evolution strategies, " Evolutionary Computation, 9, 2, pp. 159-195, 2001.
31. Herrera, F., Lozano, M., Verdegay, J. L., "Tuning Fuzzy Logic Controllers by Genetic Algorithms, " International Journal of Approximate Reasoning, 12, pp. 299-315, 1995.
32. Hinterding, R., Michalewicz, Z., Eiben, A. E., "Adaptation in Evolutionary Computation: A Survey, " in Proc. of the Fourth International Conference on Evolutionary Computation (ICEC 97), pp. 65-69, IEEE Press, New York, 1997.
33. Ho, T., "The random subspace method for constructing decision forests, " IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 8, pp. 832-844, 1998.
34. Jackowski, K., Woźniak, M., "Method of classifier selection using the genetic approach, " Expert Systems, 27, 2, pp. 114-128, 2010.
35. Juang, C.-F., Lin, J.-Y., Lin, C.-T., "Genetic reinforcement learning through symbiotic evolution for fuzzy controller design, " IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, 30, 2, pp. 290-302, 2000.
36. Kégl, B., "Robust regression by boosting the median, " in Proc. of the 16th Conference on Computational Learning Theory, pp. 258-272, 2003.
37. Kempa, O., Lasota, T., Telec, Z. and Trawiński, B., "Investigation of Bagging Ensembles of Genetic Neural Networks and Fuzzy Systems for Real Estate Appraisal, " ACIIDS 2011, LNAI 6592 (Nguyen, N. T. Kim, C.-G. and Janiak, A. eds.), pp. 323-332, Springer, Heidelberg, 2011.
38. Kim, Y.-W., Oh, I.-S., "Classifier ensemble selection using hybrid genetic algorithms, " Pattern Recognition Letters, 29, pp. 796-802, 2008.
39. Król, D., Lasota, T., Trawiński, B., Trawiń́ ski, K., "Investigation of evolutionary optimization methods of TSK fuzzy model for real estate appraisal, " International Journal of Hybrid Intelligent Systems, 5, 3, pp. 111-128, 2008.
40. Krzystanek, M., Lasota, T., Telec, Z., Trawiński, B., "Analysis of Bagging Ensembles of Fuzzy Models for Premises Valuation, " in ACIIDS 2010, Part II, LNCS (LNAI) 5991 (Nguyen, N. T., Le, M. T. and -Swi-atek, J. eds.), pp. 330-339, Springer, Heidelberg, 2010.
41. Kuncheva, L. I., Whitaker, C. J., "Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy, " Machine Learning, 51, pp. 181-207, 2003.
42. Lasota, T., Mazurkiewicz, J., Trawiński, B., Trawiński, K., "Comparison of Data Driven Models for the Validation of Residential Premises using KEEL, " International Journal of Hybrid Intelligent Systems, 7, 1, pp. 3-16, 2010.
43. Lasota, T., Telec, Z., Trawiński, B., Trawiński, K., "Exploration of Bagging Ensembles Comprising Genetic Fuzzy Models to Assist with Real Estate Appraisals, " in IDEAL 2009, LNCS 5788 (Yin, H., Corchado, E. eds.) pp. 554-561, Springer, Heidelberg, 2009.
44. Lofstrom, T., Johansson, U., Bostrom, H., "Bostrom, H.: Ensemble member selection using multi-objective optimization, " IEEE Symposium on Computational Intelligence and Data Mining, CIDM'09, pp. 245-251, 2009.
45. Maruo, M. H., Lopes, H. S., Delgado, M. R., "Self-Adapting Evolutionary Parameters Encoding Aspects for Combinatorial Optimization Problems, " in Evo-COP 2005, LNCS 3448 (Raidl, G. R., Gottlieb, J. eds.), pp. 154-165, Springer, Heidelberg, 2005.
46. Meyer-Nieberg, S., Beyer, H-G., "Self-Adaptation in Evolutionary Algorithms, " in SCI (Lobo, F. G., Lima, C. F., Michalewicz, Z. eds.), 54, pp. 47-75, Springer, Heidelberg, 2007.
47. Partalas, I., Tsoumakas, G., Vlahavas, I., "Pruning an Ensemble of Classifiers via Reinforcement Learning, " Neurocomputing, 72, 79, pp. 1900-1909, 2009.
48. Partridge, D., Yates, W. B., "Engineering multiversion neural-net systems, " Neural Computation, 8, 4, pp. 869-893, 1996.
49. Sánchez, L., Couso, I., "Combining GP operators with SA search to evolve fuzzy rule based classifiers, " Information Sciences, 136, pp. 175-192 2001.
50. Sánchez, L., Couso, I., "Fuzzy random variables-based modeling with GA-P Algorithms, " in Information, Uncertainty and Fusion (Yager, R., Bouchon-Menier, B., Zadeh, L. eds.), Kluwer Editors, pp. 245-256, 2000.
51. Sánchez, L., "A random sets-based method for identifying fuzzy models, " Fuzzy Sets and Systems, 98, 3, pp. 343-354, 1998.
52. Schaffer, J. D., Morishima, A., "An adaptive crossover distribution mechanism for genetic algorithms, " in Proc. Second Int. Conference on Genetic Algorithms, L. Erlbaum Associates Inc., New York, pp. 36-40, 1987.
53. Scherbart, A., Nattkemper, T. W., "The Diversity of Regression Ensembles Combining Bagging and Random Subspace Method, " in Advances in Neuro-Information Processing (Kppen, M., Kasabov, N., Coghill, G. eds.), ICONIP 2008, Springer, Heidelberg, pp. 911-918, 2009.
54. Smith, J. E., Fogarty, T. C., "Operator and parameter adaptation in genetic algorithms, " Soft Computing, 1, 2, pp. 81-87, 1997.
55. Srinivas, M., Patnaik, L. M., "Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms, "IEEE Trans. on Systems, Man, and Cybernetics, 24, 4, pp. 656-667, 1994.
56. Tamon, C., Xiang, J., "On the boosting pruning problem, " Proc. 11th European Conference on Machine Learning, pp. 404-412, 2000.
57. Tang, K., Li, X., Suganthan, P. N., Yang, Z., Weise, T., "Benchmark Functions for the CEC'2010 Special Session and Competition on Large Scale Global Optimization, " Technical Report, Nature Inspired Computation and Applications Laboratory, USTC, China, 2009. http://nical.ustc.edu.cn/cec10ss. php.
58. Thrift, P., "Fuzzy logic synthesis with genetic algorithms, " in Proc. of the Fourth Int. Conference on Genetic Algorithms (ICGA'91) San Diego, pp. 509-513, 1991.
59. Tsoumakas, G., Partalas, I., Vlahavas, I., "A Taxonomy and Short Review of Ensemble Selection, " in ECAI 2008, Workshop on Supervised and Unsupervised Ensemble Methods and Their Applications Patras, Greece, 2008.
60. Tsymbal, A., Pechenizkiy, M., Cunningham, P., "Diversity in search strategies for ensemble feature selection, " Information Fusion, 6, 1, pp. 83-98, 2005.
61. Wang, L. X., Mendel, J. M., "Generating Fuzzy Rules by Learning from Examples, " IEEE Trans. on Systems, Man and Cybernetics, 22, 6, pp. 1414-1427 1992.
62. Yao, X., Liu, Y., Lin, G., " Evolutionary Programming Made Faster, " IEEE Trans. Evol. Comput., 3, 2, pp. 9196, 1999.
63. Yao, X., Liu, Y., " Fast evolution strategies, " Contr. Cybern., 26, 3, pp. 467-496, 1997.