Analysis of bagging ensembles of fuzzy models for premises valuation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5991 LNAI, Issue PART 2, 2010, Pages 330-339

  Krzystanek, Marek ^a   Lasota, Tadeusz ^b   Telec, Zbigniew ^a   Trawiński, Bogdan ^a  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

^b WROC AW UNIVERSITY OF ENVIRONMENTAL AND LIFE SCIENCES   (Poland)

Author keywords

bagging; genetic fuzzy systems; KEEL; real estate appraisal

Indexed keywords

BAGGING; DATA MINING SYSTEM; DATA SETS; ENSEMBLE MEMBERS; ENSEMBLE MODELS; ERROR DIVERSITY; FUZZY ALGORITHMS; FUZZY MODELS; GENETIC FUZZY SYSTEMS; KEEL; PAIRWISE CORRELATION; REAL ESTATE; REAL-WORLD;

ALGORITHMS; DATA MINING; DATABASE SYSTEMS; PROFITABILITY;

FUZZY SETS;

EID: 77956497364     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-12101-2_34     Document Type: Conference Paper

References (31)

1. Aksela, M., Laaksonen, J.: Using diversity of errors for selecting members of a committee classifier. Pattern Recognition 39, 608-623 (2006)
2. Alcalá-Fdez, J., et al.: KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems. Soft Computing 13(3), 307-318 (2009)
3. Banfield, R.E., et al.: A Comparison of Decision Tree Ensemble Creation Techniques. IEEE Trans. on Pattern Analysis and Machine Intelligence 29(1), 173-180 (2007)
4. Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123-140 (1996)
5. Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity Creation Methods: A Survey and Categorisation. Journal of Information Fusion 6(1), 5-20 (2005)
6. Canul-Reich, J., Shoemaker, L., Hall, L.O.: Ensembles of Fuzzy Classifiers. In: Proc. IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2007, pp. 1-6 (2007)
7. Chandra, A., Yao, X.: Evolving hybrid ensembles of learning machines for better generalisation. Neurocomputing 69, 686-700 (2006)
8. Chawla, N.V., Hall, L.O., Bowyer, K.W., Kegelmeyer, W.P.: Learning Ensembles From Bites: A Scalable and Accurate Approach. Journal of Machine Learning Research 5, 421-451 (2004)
9. Chen, T., Ren, J.: Bagging for Gaussian Process Regression. Neurocomputing 72(7-9), 1605-1610 (2009)
10. 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. Int. J. Hybrid Intelligent Systems (2009) (in press)
11. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1-30 (2006) (Pubitemid 43022939)
12. Dos Santos, E.M., Sabourin, R., Maupin, P.: A dynamic overproduce-and-choose strategy for the selection of classifier ensembles. Pattern Recognition 41(10), 2993-3009 (2008)
13. Freund, Y., Schapire, R.E.: Decision-theoretic generalization of on-line learning and an application to boosting. J. Computer and System Sciences 55(1), 119-139 (1997)
14. García-Pedrajas, N.: Constructing Ensembles of Classifiers by Means of Weighted Instance Selection. IEEE Transactions on Neural Networks 20(2), 258-277 (2009)
15. Hernandez-Lobato, D., Martinez-Munoz, G., Suarez, A.: Pruning in ordered regression bagging ensembles. In: Yen, G.G. (ed.) Proceedings of the IEEE World Congress on Computational Intelligence, pp. 1266-1273 (2006) (Pubitemid 351369463)
16. Islam, M.M., et al.: Bagging and Boosting Negatively Correlated Neural Networks. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cyb. 38(3), 771-784 (2008)
17. Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Computation 3, 79-87 (1991)
18. Kim, D.: Improving the Fuzzy System Performance by Fuzzy System Ensemble. Fuzzy Sets and Systems 98(1), 43-56 (1998)
19. Król, D., Lasota, T., Trawiski, B., Trawiski, K.: Investigation of Evolutionary Optimization Methods of TSK Fuzzy Model for Real Estate Appraisal. International Journal of Hybrid Intelligent Systems 5(3), 111-128 (2008)
20. Krzystanek, M., Lasota, T., Trawiski, B.: Comparative Analysis of Evolutionary Fuzzy Models for Premises Valuation Using KEEL. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol.5796, pp. 838-849. Springer, Heidelberg (2009)
21. Kuncheva, L.I., Whitaker, C.J.: Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy. Machine Learning 51, 181-207 (2003)
22. Lasota, T., Mazurkiewicz, J., Trawiski, B., Trawiski, K.: Comparison of Data Driven Models for the Validation of Residential Premises Using KEEL. International Journal of Hybrid Intelligent Systems (2009) (in press)
23. Lasota, T., Telec, Z., Trawiski, B., Trawiski, K.: A Multi-agent System to Assist with Real Estate Appraisals using Bagging Ensembles. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol.5796, pp. 813-824. Springer, Heidelberg (2009)
24. Lasota, T., Telec, Z., Trawiski, B., Trawiski, K.: Exploration of Bagging Ensembles Comprising Genetic Fuzzy Models to Assist with Real Estate Appraisals. In: Corchado, E., Yin, H. (eds.) IDEAL 2009. LNCS, vol.5788, pp. 554-561. Springer, Heidelberg (2009)
25. Liu, Y., Yao, X.: Ensemble learning via negative correlation. Neural Networks 12(10), 1399-1404 (1999)
26. Margineantu, D.D., Dietterich, T.G.: Pruning Adaptive Boosting. In: Proc. 14th Int. Conf. Machine Learning, pp. 211-218 (1997)
27. Polikar, R.: Ensemble Learning. Scholarpedia 4(1), 2776 (2009)
28. Rokach, L.: Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography. Comp. Stat. and Data Analysis 53, 4046-4072 (2009)
29. Schapire, R.E.: The Strength of Weak Learnability. Machine Learning 5(2), 197-227 (1990)
30. Wolpert, D.H.: Stacked Generalization. Neural Networks 5(2), 241-259 (1992)
31. Zhou, Z.H., Wu, J., Tang, W.: Ensembling Neural Networks: Many Could Be Better Than All. Artificial Intelligence 137, 239-263 (2002)