Dynamic selection and circulating combination for multiple classifier systems

Zidonghua Xuebao/Acta Automatica Sinica

Volumn 37, Issue 11, 2011, Pages 1290-1295

  Hao, Hong Wei ^a   Wang, Zhi Bin ^a   Yin, Xu Cheng ^a   Chen, Zhi Qiang ^b  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING   (China)

^b Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases   (China)

Author keywords

Circulating combination; Complementarity factor; Dynamic selection; Multiple classifier systems

Indexed keywords

CIRCULATING COMBINATION; CLASSIFICATION MODELS; CLASSIFIER SELECTION; COMBINING CLASSIFIERS; COMPLEMENTARITY FACTOR; DYNAMIC SELECTION; HANDWRITTEN DIGIT RECOGNITION; MULTIPLE CLASSIFIER SYSTEMS; OPTIMAL SUBSETS;

OPTIMIZATION;

CHARACTER RECOGNITION;

EID: 83255181453     PISSN: 02544156     EISSN: None     Source Type: Journal    
DOI: 10.3724/SP.J.1004.2011.01290     Document Type: Article

References (21)

1. Fumera G, Roli F, Serrau A. A theoretical analysis of bagging as a linear combination of classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(7): 1293-1299
2. Hao H W, Liu C L, Sako H. Comparison of genetic algorithm and sequential search methods for classifier subset selection. In: Proceedings of the 7th International Conference on Document Analysis and Recognition. Edinburgh, Scotland: IEEE, 2003. 765-769
3. Brown G. An information theoretic perspective on multiple classifier systems. In: Proceedings of the 8th International Workshop on Multiple Classifier Systems. Reykjavik, Iceland: Springer-Verlag, 2009. 344-353
4. Ruta D, Gabrys B. Classifier selection for majority voting. Information Fusion, 2005, 6(1): 63-81
5. Zhou Z H, Wu J X, Tang W. Ensembling neural networks: many could be better than all. Artificial Intelligence, 2002, 137(1-2): 239-263
6. Kang H J, Doermann D. Selection of classifiers for the construction of multiple classifier systems. In: Proceedings of the 8th International Conference on Document Analysis and Recognition. Seoul, Korea: IEEE, 2005. 1194-1198
7. Ko A H R, Sabourin R, Britto A S. From dynamic classifier selection to dynamic ensemble selection. Pattern Recognition, 2008, 41(5): 1735-1748
8. Chen L, Kamel M S. A generalized adaptive ensemble generation and aggregation approach for multiple classifier systems. Pattern Recognition, 2009, 42(5): 629-644
9. Liu R, Yuan B. Multiple classifier combination by clustering and selection. Information Fusion, 2001, 2(3): 163-168
10. Li Guo-Zheng, Yang Jie, Kong An-Sheng, Chen Nian-Yi. Clustering algorithm based selective ensemble. Journal of Fudan University (Natural Science), 2004, 43(5): 689-691
11. Kim Y W, Oh I S. Classifier ensemble selection using hybrid genetic algorithms. Pattern Recognition Letters, 2008, 29(6): 796-802
12. Santos E M, Sabourin R, Maupin P. Over fitting cautious selection of classifier ensembles with genetic algorithms. Information Fusion, 2009, 10(2): 150-162
13. Jackowski K, Wozniak M. Method of classifier selection using the genetic approach. Expert Systems, 2010, 27(2): 114-128
14. Banfield R E, Hall L O, Bowyer K W, Kegelmeyer W P. Ensemble diversity measures and their application to thinning. Information Fusion, 2005, 6(1): 49-62
15. Didaci L, Giacinto G, Foli F, Marcialis G L. A study on the performances of dynamic classifier selection based on local accuracy estimation. Pattern Recognition, 2005, 38(11): 2188-2191
16. Didaci L, Giacinto G. Dynamic classifier selection by adaptive X-nearest neighborhood rule. In: Proceedings of the 5th International Workshop on Multiple Classifier Systems. Cagliari, Italy: Springer-Verlag, 2004. 174-183
17. Kuncheva L I, Whitaker C J. Measures of diversity in classifier ensemble and their relationship with the ensemble accuracy. Machine Learning, 2003, 51(2): 181-207
18. Liu C L, Hao H W, Sako H. Confidence transformation for combining classifiers. Pattern Analysis and Application, 2004, 7(1): 2-17
19. Kherallah M, Haddad L, Alimi A M, Mitiche A. On-line handwritten digit recognition based on trajectory and velocity modeling. Pattern Recognition Letters, 2008, 29(5): 580-594
20. Chen Z. Handwritten digits recognition. In: Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition. Las Vegas, USA: CSREA, 2009. 690-694
21. Hao Hong-Wei, Jiang Rong-Rong. Training sample selection method for neural networks based on nearset neighbor rule. Acta Automatica Sinica, 2007, 33(12): 1247-1251