Statistical instance-based pruning in ensembles of independent classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 31, Issue 2, 2009, Pages 364-369

  Hernández Lobato, Daniel ^a   Martinez Muñoz, Gonzalo ^a   Suárez, Alberto ^a  

^a UNIVERSIDAD AUTÓNOMA DE MADRID   (Spain)

Author keywords

Bagging; Ensemble learning; Ensemble pruning; Instance based pruning; Polya urn; Random forests

Indexed keywords

BAGGING; ENSEMBLE LEARNING; ENSEMBLE PRUNING; INSTANCE-BASED PRUNING; POLYA URN; RANDOM FORESTS;

EDUCATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; PROBABILITY DENSITY FUNCTION;

CLASSIFIERS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; DECISION SUPPORT SYSTEM; METHODOLOGY; STATISTICAL ANALYSIS; STATISTICAL MODEL; THEORETICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; DECISION SUPPORT TECHNIQUES; MODELS, STATISTICAL; MODELS, THEORETICAL; PATTERN RECOGNITION, AUTOMATED;

EID: 62249197666     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2008.204     Document Type: Article

References (21)

1. Y. Freund and R.E. Schapire, "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting," Proc. Second European Conf. Computational Learning Theory, pp. 23-37, 1995.
2. L. Breiman, "Bagging Predictors," Machine Learning, vol. 24, no. 2, pp. 123-140, 1996.
3. T.G. Dietterich, "An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization," Machine Learning, vol. 40, no. 2, pp. 139-157, 2000.
4. L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.
5. P. Buhlmann, "Bagging, Subagging and Bragging for Improving Some Prediction Algorithms," Recent Advances and Trends in Nonparametric Statistics, M. Akritas and D. Politis, eds., pp. 19-34, 2003.
6. G. Martínez-Muñoz and A. Suárez, "Switching Class Labels to Generate Classification Ensembles," Pattern Recognition, vol. 38, no. 10, pp. 1483-1494, 2005.
7. J.J. Rodriguez, L.I. Kuncheva, and C.J. Alonso, "Rotation Forest: A New Classifier Ensemble Method," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 10, pp. 1619-1630, Oct. 2006.
8. R.E. Banfield, L.O. Hall, K.W. Bowyer, and W.P. Kegelmeyer, "A Comparison of Decision Tree Ensemble Creation Techniques," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 173-180, Jan. 2007.
9. L.K. Hansen and P. Salamon, "Neural Network Ensembles," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, pp. 993-1001, 1990.
10. L. Lam and C. Suen, "Application of Majority Voting to Pattern Recognition: An Analysis of Its Behavior and Performance," IEEE Trans. Systems, Man, and Cybernetics, vol. 27, no. 5, pp. 553-568, 1997.
11. R. Esposito and L. Saitta, "Monte Carlo Theory as an Explanation of Bagging and Boosting," Proc. 18th Int'l Joint Conf. Artificial Intelligence, pp. 499-504, 2003.
12. W. Fan, F. Chu, H. Wang, and P.S. Yu, "Pruning and Dynamic Scheduling of Cost-Sensitive Ensembles," Proc. 18th Nat'l Conf. Artificial Intelligence, pp. 146-151, 2002.
13. H. Wang, W. Fan, P.S. Yu, and J. Han, "Mining Concept-Drifting Data Streams Using Ensemble Classifiers," Proc. Ninth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 226-235, 2003.
14. R. Schapire, Y. Freund, P. Bartlett, and W. Lee, "Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods," The Annals of Statistics, vol. 12, no. 5, pp. 1651-1686, 1998.
15. C.M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, Aug. 2006.
16. M. Abramowitz and I.A. Stegun, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Dover, 1964.
17. N.L. Johnson, S. Kotz, and N. Balakrishnan, Discrete Multivariate Distributions. John Wiley & Sons, 1997.
18. A. Asuncion and D. Newman, "UCI Machine Learning Repository," http://www.ics.uci.edu/mlearn/MLRepository.html, 2007.
19. L. Breiman, "Bias, Variance, and Arcing Classifiers," Technical Report 460, Statistics Dept., Univ. of California, Berkeley, 1996.
20. I. Hedenfalk, D. Duggan, Y. Chen, M. Radmacher, M. Bittner, R. Simon, P. Meltzer, B. Gusterson, M. Esteller, O.P. Kallioniemi, B. Wilfond, A. Borg, and J. Trent, "Gene-Expression Profiles in Hereditary Breast Cancer," The New England J. Medicine, vol. 344, no. 8, pp. 539-548, 2001.
21. J. Demsǎr, "Statistical Comparisons of Classifiers over Multiple Data Sets," J. Machine Learning Research, vol. 7, pp. 1-30, 2006.