Ensembling heterogeneous learning models with boosting

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

Volumn 5863 LNCS, Issue PART 1, 2009, Pages 512-519

  Nascimento, Diego S C ^a   Coelho, André L V ^a  

^a UNIVERSITY OF FORTALEZA   (Brazil)

Author keywords

Boosting; Genetic algorithms; Heterogeneous models

Indexed keywords

ADABOOST; AUTOMATIC DESIGN; BOOSTING ALGORITHM; CLASSIFICATION DATASETS; CLASSIFIER ENSEMBLES; DIVERSITY LEVEL; HETEROGENEOUS MODELS; LEARNING MODELS; RBF NEURAL NETWORK; UCI REPOSITORY;

ADAPTIVE BOOSTING; CLASSIFICATION (OF INFORMATION); DATA PROCESSING; GENETIC ALGORITHMS; HYSTERESIS MOTORS; NEURAL NETWORKS;

MATHEMATICAL MODELS;

EID: 76649135491     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-10677-4_58     Document Type: Conference Paper

References (15)

1. Asunción, A., Newman, D.J.: UCI Machine Learning Repository. University of California at Irvine (2007), http://ics.uci.edu/~mlearn/ MLRepository.html
2. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123-140 (1996)
3. Canuto, A., Abreu, M., Oliveira, L., Xavier Jr., J., Santos, A.: Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles. Pattern Recognit. Lett. 28(4), 472-486 (2007)
4. Canuto, A., Oliveira, L., Xavier Jr., J., Santos, A., Abreu, M.: Performance and diversity evaluation in hybrid and non-hybrid structures of ensembles. In: Procs. of the Fifth Int. Conf. on Hybrid Intelligent Systems, pp. 285-290 (2005)
5. Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1-15. Springer, Heidelberg (2000)
6. Eiben, A., Smith, J.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)
7. Freund, Y., Schapire, R.: A decision-theoretic generalization of online learning and an application to boosting. J. Comput. Syst. Sci 55, 119-139 (1997)
8. Haykin, S.: Neural Networks-A Comprehensive Foundation. Prentice-Hall, Englewood Cliffs (1999)
9. Kotsiantis, S.B., Pintelas, P.E.: Combining bagging and boosting. Int. J. Comput. Intell. 1(4), 324-333 (2004)
10. Menahem, E., Shabtai, A., Rokach, L., Elovici, Y.: Improving malware detection by applying multi-inducer ensemble. Comput. Stat. Data Anal. 53, 1483-1494 (2009)
11. Opitz, D., Maclin, R.: Popular ensemble methods: An empirical study. J. Artif. Intell. Res. 11, 169-198 (1999)
12. Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21-45 (2006)
13. Webb, G.I.: Multiboosting: A technique combining boosting and wagging. Mach. Learn. 40, 159-196 (2000)
14. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Elsevier, Amsterdam (2005)
15. Zhou, Z.-H., Wu, J., Tang, W.: Ensembling neural networks: Many could be better than all. Artif. Intell. 137(1-2), 239-263 (2002)