A PSO algorithm for improving multi-view classification

2011 IEEE Congress of Evolutionary Computation, CEC 2011

Volumn , Issue , 2011, Pages 925-932

  Cordeiro, Zilton ^a   Pappa, Gisele L ^a  

^a FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; DEMPSTER-SHAFER THEORY; LEARNING APPROACH; MAJORITY VOTING; MULTI-MODALITY; MULTI-VIEW LEARNING; MULTI-VIEWS; PSO ALGORITHMS; VIDEO CLASSIFICATION; WEIGHTING SCHEME;

FORMAL LOGIC;

EVOLUTIONARY ALGORITHMS;

EID: 80051977632     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2011.5949717     Document Type: Conference Paper

References (37)

1. J. Baxter. Theoretical models of learning to learn. pages 71-94, 1998.
2. F. Benevenuto, T. Rodrigues, V. Almeida, J. Almeida, and M. Gonalves. Detecting spammers and content promoters in online video social networks. in Proc. of Int'l ACM SIGIR, Boston, MA, USA, July 2009.
3. Y. Bi, S. McClean, and T. Anderson. On combining multiple classifiers using an evidential approach. in AAAI'06: Proceedings of the 21st national conference on Artificial intelligence, pages 324-329. AAAI Press, 2006. (Pubitemid 44705305)
4. A. Blum and T. Mitchell. Combining labeled and unlabeled data with cotraining. in Proc. of the 11th Annual Conf. on Computational Learning theory, pages 92-100, 1998.
5. D. Bremner, E. Demaine, J. Erickson, J. Iacono, S. Langerman, P. Morin, and G. Toussaint. Output-sensitive algorithms for computing nearestneighbour decision boundaries. Discrete Comput. Geom., 33:593-604, April 2005. (Pubitemid 40550561)
6. M. W. Browne. Cross-validation methods. Journal of Mathematical Psychology, 44(1):108-132, 2000.
7. E. R. C., S. Yuhui, and K. James. Swarm Intelligence. Morgan Kaufmann, 2001.
8. Q. Cao and Y. Liu. A knn classifier with pso feature weight learning ensemble. in Intelligent Control and Information Processing (ICICIP), 2010 International Conference on, pages 110-114, 2010.
9. M. Culp and G. Michailidis. A co-training algorithm for multi-view data with applications in data fusion. Journal of chemometrics, 23:294-303, 2009.
10. T. Dietterich. Ensemble methods in machine learning. in Multiple Classifier Systems, volume 1857 of Lecture Notes in Computer Science, pages 1-15. Springer Berlin/Heidelberg, 2000.
11. J.-X. Du, D.-S. Huang, and Z.-F. Wang. Pattern classification with a pso optimization based elliptical basis function neural networks. in Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, pages 1654-1661, 2007.
12. Y. Freund and R. Schapire. A desicion-theoretic generalization of online learning and an application to boosting. in Computational Learning Theory, volume 904 of Lecture Notes in Computer Science, pages 23-37. Springer Berlin/Heidelberg, 1995.
13. W. Fujibuchi and T. Kato. Classification of heterogeneous microarray data by maximum entropy kernel. BMC Bioinformatics, 8:267+, July 2007.
14. V.-N. Huynh, T. T. Nguyen, and C. A. Le. Adaptively entropy-based weighting classifiers in combination using dempster-shafer theory for word sense disambiguation. Comput. Speech Lang., 24:461-473, July 2010.
15. A. Kausar, M. Ishtiaq, M. A. Jaffar, and A. M. Mirza. Optimization of ensemble based decision using pso. in Proceedings of the World Congress on Engineering, WCE'10, 2010.
16. N. Khan, A. Rauf Baig, and M. Iqbal. A new discrete pso for data classification. in Information Science and Applications (ICISA), 2010 International Conference on, pages 1-6, 2010.
17. A. Khurshid and A. Gokhale. Classification system for digital signal types using neuro fuzzy system and pso. in Nature Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, pages 373-378, 2009.
18. L. Kuncheva. Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognition, 34(2):299-314, February 2001. (Pubitemid 32871876)
19. S. Li, X. Peng, X. Hou, H. Zhang, and Q. Cheng. Multi-view face pose estimation based on supervised isa learning. in Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE International Conference on, pages 100-105, May 2002.
20. A. McCallum and K. Nigam. A comparison of event models for naive bayes text classification. in Proc. of the 15th Nat. Conf. on Artificial Intelligence, pages 41-48, 1998.
21. A. Mohemmed, M. Johnston, and M. Zhang. Particle swarm optimization based multi-prototype ensembles. in Proceedings of the 11th Annual conference on Genetic and evolutionary computation, GECCO'09, pages 57-64, New York, NY, USA, 2009. ACM.
22. I. M. Muslea, S. Minton, and C. A. Knoblock. Active + semi-supervised learning = robust multi-view learning. in Proc. of 19th Int. Conf. on Machine Learning, pages 435-442, 2002.
23. S. Nabavi-Kerizi, M. Abadi, and E. Kabir. A pso-based weighting method for linear combination of neural networks. Computers & Electrical Engineering, 36(5):886-894, 2010. Advances in Computing Systems Science and Engineering.
24. V. Ng and C. Cardie. Weakly supervised natural language learning without redundant views. in Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1, NAACL'03, pages 94-101, Stroudsburg, PA, USA, 2003. Association for Computational Linguistics.
25. K. Nigam, A. K. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using EM. Mach. Learn., 39(2-3):103-134, 2000. (Pubitemid 30594822)
26. C. A. Perez, L. A. Cament, and L. E. Castillo. Methodological improvement on local gabor face recognition based on feature selection and enhanced borda count. Pattern Recognition, 44(4):951-963, 2011.
27. A. Rakitianskaia and A. P. Engelbrecht. Training neural networks with pso in dynamic environments. in Proceedings of the Eleventh conference on Congress on Evolutionary Computation, CEC'09, pages 667-673, Piscataway, NJ, USA, 2009. IEEE Press.
28. J. Rocchio. Relevance feedback in information retrieval. The SMART retrieval system: experiments in automatic document processing, pages 313-323, 1971.
29. G. Shafer. A mathematical theory of evidence. Princeton university press, 1976.
30. H. Tong, J. He, M. Li, C. Zhang, and W.-Y. Ma. Graph based multimodality learning. in MULTIMEDIA'05: Proc. of the 13th annual ACM international conference on Multimedia, pages 862-871, New York, NY, USA, 2005. ACM.
31. R. J. Urbanowicz and J. H. Moore. The application of michiganstyle learning classifier systems to address genetic heterogeneity and epistasisin association studies. in Proceedings of the 12th annual conference on Genetic and evolutionary computation, GECCO'10, pages 195-202, New York, NY, USA, 2010. ACM.
32. V. N. Vapnik. The nature of statistical learning theory. Springer-Verlag, 1995.
33. Z. Wang, S. Chen, H. Xue, and Z. Pan. A novel regularization learning for single-view patterns: Multi-view discriminative regularization. Neural Processing Letters, 31:159-175, 2010.
34. L. Xu, J.-H. Jiang, H.-L. Wu, G.-L. Shen, and R.-Q. Yu. Variableweighted pls. Chemometrics and Intelligent Laboratory Systems, 85(1):140-143, 2007.
35. S. Yu, B. Krishnapuram, R. Rosales, H. Steck, and B. R. Rao. Bayesian co-training. In J. C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20, pages 1665-1672. MIT Press, Cambridge, MA, 2008.
36. X. Zhou, J. Yuyu, R. Qi, F. Qian, and Z. Wang. Ipso: An immune based pso supervised learning system for incremental learning. in Intelligent Control and Automation (WCICA), 2010 8th World Congress on, pages 2707-2711, 2010.
37. H.-Y. Zou, H.-L. Wu, H.-Y. Fu, L.-J. Tang, L. Xu, J.-F. Nie, and R.-Q. Yu. Variable-weighted least-squares support vector machine for multivariate spectral analysis. Talanta, 80(5):1698-1701, 2010.