Pareto ensemble pruning

Proceedings of the National Conference on Artificial Intelligence

Volumn 4, Issue , 2015, Pages 2935-2941

  Qian, Chao ^a   Yu, Yang ^a   Zhou, Zhi Hua ^a  

^a NANJING UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; OPTIMIZATION;

ENSEMBLE LEARNING; ENSEMBLE PRUNING; EVOLUTIONARY OPTIMIZATIONS; GENERALIZATION PERFORMANCE; LEARNING TECHNIQUES; MINIMIZING THE NUMBER OF; STATE OF THE ART; STATE-OF-THE-ART APPROACH;

LEARNING SYSTEMS;

EID: 84949912487     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Anguita, D.; Ghio, A.; Oneto, L.; Parra, X.; and Reyes-Ortiz, J. L. 2012. Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In Proceedings of 1 he 4lh International Workshop on Ambient Assisted Living and Home Care, 216-223.
2. Auger, A., and Doerr, B. 2011. Theory of Randomized Search Heuristics: Foundations and Recent Developments. Singapore: World Scientific.
3. Back, T. 1996. Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford, UK: Oxford University Press.
4. Banfield, R. E.; Hall, L. O.; Bowyer, K. W.; and Kegelmeyer, W. P. 2005. Ensemble diversity measures and their application to thinning. Information Fusion 6(1):49-62.
5. Blake, C. L.; Keogh, E.; and Merz, C. J. 1998. UCI Repository of machine learning databases. [http://www.ics.uci.edu/" mlearn/MLRepository.html]
6. Breiman, L. 1996. Bagging predictors. Machine Learning 24(2):123-140.
7. Brown, G.; Wyatt, J.; Harris, R.; and Yao, X. 2005. Diversity creation methods: A survey and categorisation. Information Fusion 6(1):5-20.
8. Caruana, R.; Niculescu-Mizil, A.; Crew, G.; and Ksikes, A. 2004. Ensemble selection from libraries of models. In Proceedings of the 21st International Conference on Machine Learning, 18-25.
9. Castro, P. D.; Coelho, G. P.; Caetano, M. P.; and Von Zuben, F. J. 2005. Designing ensembles of fuzzy classification systems: An immune-inspired approach. In Proceedings of the 4th International Conference on Artificial Immune Systems, 469-482.
10. Demšar, J. 2006. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7:1-30.
11. He, J., and Yao, X. 2001. Drift analysis and average time complexity of evolutionary algorithms. Artificial Intelligence 127(1):57-85.
12. Hernández-Lobato, D.; Marttnez-Munoz, G.; and Suarez, A. 2011. Empirical analysis and evaluation of approximate techniques for pruning regression bagging ensembles. Neurocomputing 74(12):2250-2264.
13. Li, N., and Zhou, Z.-H. 2009. Selective ensemble under regularization framework. In Proceedings of the 8th International Workshop on Multiple Classifier Systems, 293-303.
14. Li, N.; Yu, Y.; and Zhou, Z.-H. 2012. Diversity regularized ensemble pruning. In Proceedings of the 23rd European Conference on Machine Learning, 330-345.
15. Lin, S., and Kernighan, B. W. 1973. An effective heuristic algorithm for the travel ing-salesman problem. Operations Research 21(2):498-516.
16. Margineantu, D. D., and Dietterich, T. G. 1997. Pruning adaptive boosting. In Proceedings of the I4th International Conference on Machine Learning, 211-218.
17. Martínez-Muñoz, G.; Hernandez-Lobato, D.; and Suarez, A. 2009. An analysis of ensemble pruning techniques based on ordered aggregation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(2):245-259.
18. Partalas, I.; Tsoumakas, G.; and Vlahavas, I. 2012. A study on greedy algorithms for ensemble pruning. Technical report, Aristotle University of Thessaloniki, Greece.
19. Qian, C.; Yu, Y.; and Zhou, Z.-H. 2013. An analysis on recombination in multi-objective evolutionary optimization. Artificial Intelligence 204:99-119.
20. Quinlan, J. R. 1993. C4.5: Programs for Machine Learning. San Francisco, CA: Morgan kaufmann.
21. Tsoumakas, G.; Partalas, I.; and Vlahavas, I. 2009. An ensemble pruning primer. In Applications of Supervised and Unsupervised Ensemble Methods, volume 245 of Studies in Computational Intelligence. Berlin, Germany: Springer. 1-13.
22. Yu, Y.; Yao, X.; and Zhou, Z.-H. 2012. On the approximation ability of evolutionary optimization with application to minimum set cover. Artificial Intelligence 180-181:20-33.
23. Zhang, Y.; Burer, S.; and Street, W. N. 2006. Ensemble pruning via semi-definite programming. Journal of Machine Learning Research 7:1315-1338.
24. Zhou, Z.-H.; Wu, J.; and Tang, W. 2002. Ensembling neural networks: Many could be better than all. Artificial Intelligence 137(1):239-263.
25. Zhou, Z.-H. 2012. Ensemble Methods: Foundations and Algorithms. Boca Raton, FL: Chapman & Hall/CRC.