Selective neural network ensemble based on clustering

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

Volumn 3971 LNCS, Issue , 2006, Pages 545-550

  Chen, Haixia ^a   Yuan, Senmiao ^a   Jiang, Kai ^b  

^a JILIN UNIVERSITY   (China)

^b CETC   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS; DATABASE SYSTEMS; LEARNING SYSTEMS; OPTIMIZATION;

CLASSIFICATION ACCURACIES; DATA SETS; SELECTIVE NEURAL NETWORKS;

NEURAL NETWORKS;

EID: 33745899305     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11759966_81     Document Type: Conference Paper

References (11)

1. Krogh, A., Vedelsby, J.: Neural Network Ensembles, Cross Validation, and Active Learning. In: Tesauro, G., Touretzky, D. and Leen, T. Eds. Advances In Neural Information Processing Systems 7, Cambridge, MA: MIT Press. (1995) 231-238
2. Kuncheva, L.I., Skurichina, M., Duin, R.P.W.: An Experimental Study on Diversity for Bagging and Boosting with Linear Classifiers. Information Fusion 3 (2002) 245-258
3. Bauer, E., Kohavi, R.: An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning 36(1,2) (1999) 105-139
4. Brodley, C., Lane, T.: Creating and Exploiting Coverage and Diversity. In: Proc. AAAI-96 Workshop on Integrating Multiple Learned Models (1996) 8-14
5. th Int'l Conf. on Pattern Recognition, Barcelona, Spain (2000) 3-8
6. Patridge, D., Yates, W.B.: Engineering Multiversion Neural-Net Systems. Neural Computation 8(4) (1996) 869-893
7. Rätsch, G., Onoda, T., Müller, K.R.: Soft Margins for Adaboost. Machine Learning 42(3) (2001) 287-320
8. Oliveira, L.S., Morita, M., Sabourin, R., Bortolozzi, F.: Multi-Objective Genetic Algorithms To Create Ensemble of Classifiers. C.A. Coello Coello et al. (Eds.) EMO 2005, LNCS 3410 (2005) 592-606
9. Ho, T.K.: The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8) (1998) 832-844
10. Chindaro, S., Sirlantzis, K., Fairhurst, M.: Analysis and Modeling of Diversity Contribution to Ensemble-Based Texture Recognition Performance. N.C. Oza et al. (Eds.): MCS 2005, LNCS 3541 (2005) 387-396
11. Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases at http://www. ics.uci.edu/-mlearn/MLRepository.html. Irvine, CA: University of California, Dept. of Information and Computer Science (1998)