Ambiguity-guided dynamic selection of ensemble of classifiers

FUSION 2007 - 2007 10th International Conference on Information Fusion

Volumn , Issue , 2007, Pages

  Dos Santos, Eulanda M ^a   Sabourin, Robert ^a   Maupin, Patrick ^b  

^a ÉCOLE DE TECHNOLOGIE SUPÉRIEURE   (Canada)

^b DEFENCE RESEARCH AND DEVELOPMENT CANADA   (Canada)

Author keywords

Diversity measures; Dynamic selection; Ensemble of classifiers; Genetic algorithms

Indexed keywords

CLASSIFIERS; DATA FUSION; DISTRIBUTED PARAMETER NETWORKS; FUSION REACTIONS; INFORMATION FUSION; LEARNING SYSTEMS; NUCLEAR PHYSICS; SEMANTICS; SENSOR DATA FUSION; TESTING;

DIVERSITY MEASURES; DYNAMIC CLASSIFIER SELECTION; DYNAMIC SELECTION; ENSEMBLE OF CLASSIFIERS; GENERALIZATION PERFORMANCE; GENETIC ALGORITHMS; INTERNATIONAL CONFERENCES; SELECTION STRATEGIES; TEST PATTERNS;

CLASSIFICATION (OF INFORMATION);

EID: 50149102811     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIF.2007.4408123     Document Type: Conference Paper

References (17)

1. L. Breiman, "Machine Learning," Machine Learning, vol. 24, no. 2, pp. 123-140, 1996.
2. Y. Freund and R. Schapire, "Experiments with a new boosting algorithm," in XIII International Conference on Machine Learning, 1996, pp. 148-156.
3. T.K. Ho, "The Random Subspace Method for Constructing Decision Forests," IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI, vol. 20, no. 8, pp. 832-844, 1998.
4. K. Woods and W.P. Kegelmeyer and K. Bowyer, "Combination of Multiple Classifiers Using Local Accuracy Estimates," IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI, vol. 19, no. 4, pp. 405-410, 1997.
5. E.M. Dos Santos and R. Sabourin and P. Maupin, "Single and Multi-Objective Genetic Algorithms for the Selection of Ensemble of Classifiers," in International Joint Conference on Neural Networks, Vancouver-Canada, 2006, pp.5377-5384.
6. D. Ruta and B. Gabrys, "Classifier Selection for Majority Voting," Information Fusion, vol. 6, no. 1, pp. 163-168, 2005.
7. G. Giacinto and F. Roli, "Dynamic classifier selection based on multiple classifier behaviour," Pattern Recognition, vol. 34, pp. 1879-1881, 2001.
8. D. Corne and J. Knowles, "No free lunch and free leftovers theorems for multiobjective optimization problems," in Evolutionary Multi-Criterion Optimization, 2003, pp. 327-341.
9. X. Zhu and X. Wu and Y. Yang, "Dynamic Selection for Effective Mining from Noisy Data Streams," in Fourth IEEE International Conference on Data Mining, 2004, pp. 305-312.
10. L.I. Kuncheva, "Cluster-and-Selection Model for Classifier Combination," in International Conference on Knowledge Based Intelligent Engineering Systems and Allied Technologies, 2000, pp. 185-188.
11. K. Deb,Multi-Objective Optimization using Evolutionary Algorithms, England:John Wiley & Sons, LTD, 2001.
12. H.W. Shin and S.Y. Sohn, "Selected tree classifier combination based on both accuracy and error diversity," Pattern Recognition, vol. 38, pp.191-197, 2005.
13. G. Zenobi and P. Cunningham, "Using diversity in preparing ensembles of classifiers based on different feature subsets to minimize generalization error," in European Conference on Machine Learning, 2001, pp. 576-587.
14. L.K. Hansen and C. Liisber and P. Salomon, "The Error-Reject Tradeoff," Open Systems & Information Dynamics, vol. 4, pp. 159-184, 1997.
15. L.I. Kuncheva and C.J. Whitaker, "Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy," Machine Learning, vol. 51, no. 2, pp.181-207, 2003.
16. L. Didaci, G. Giacinto, F. Roli and G.L. Marcialis, "A study on the performances of the dynamic classifier selection based on local accuracy estimation," Pattern Recognition, vol. 28, pp.2188-2191, 2005.
17. J.R. Quinlan,Programs for Machine Learning, Morgan Kaufmann, 1993.