A dynamic optimization approach for adaptive incremental learning

International Journal of Intelligent Systems

Volumn 26, Issue 11, 2011, Pages 1101-1124

  Kapp, Marcelo N ^a,b   Sabourin, Robert ^a   Maupin, Patrick ^c  

^a ÉCOLE DE TECHNOLOGIE SUPÉRIEURE   (Canada)

^b Avenida Tarquínio Joslin dos Santos   (Brazil)

^c DEFENCE RESEARCH AND DEVELOPMENT CANADA VALCARTIER   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION ENVIRONMENT; CHANGE DETECTION; CLASSIFICATION METHODS; CLASSIFICATION SYSTEM; CONFIDENCE LEVELS; DYNAMIC ENSEMBLE; DYNAMIC OPTIMIZATION; FUNDAMENTAL PROBLEM; INCREMENTAL LEARNING; INCREMENTAL SUPPORT VECTOR MACHINE; REAL-WORLD DATABASE; SELECTION BASED;

OPTIMIZATION;

EVOLUTIONARY ALGORITHMS;

EID: 80053053621     PISSN: 08848173     EISSN: 1098111X     Source Type: Journal    
DOI: 10.1002/int.20501     Document Type: Article

References (36)

1. Kuncheva LI,. Classifier ensembles for changing environments. In: Proc 5th Int Workshop on Multiple Classifier Systems; 2004.
2. Kelly MG, Hand DJ, Adams NM,. The impact of changing populations on classifier performance. In: Proc 5th Int Conf on Knowledge Discovery and Data Mining; 1999.
3. Syed NA, Liu H, Sung KK,. Handling concept drifts in incremental learning with support vector machines. In: Proc 5th Int Conf on Knowledge Discovery and Data Mining; 1999.
4. Carpenter GA, Grossberg S, Markuzon N, Reynolds JH, Rosen DB,. Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans Neural Netw. 1992; 3 (3): 698-713. (Pubitemid 23555782)
5. Polikar R, Udpa L, Udpa SS, Honavar V,. Learn++: an incremental learning algorithm for supervised neural networks. IEEE Trans Syst Man Cybernet. 2001; 31 (31): 497-508. (Pubitemid 34191829)
6. Cohen L, Avrahami-Bakish G, Last M, Kandel A, Kipersztok O,. Real-time data mining of non-stationary data streams from sensor networks. Inform Fusion. 2008; 9 (9): 344-353. (Pubitemid 351680781)
7. Parikh D, Polikar R,. An ensemble-based incremental learning approach to data fusion. IEEE Trans Syst Man Cybern, Part B: Cybernet. 2007; 37 (37): 437-450. (Pubitemid 46523231)
8. Valentini G,. An experimental bias-variance analysis of SVM ensembles based on resampling techniques. IEEE Trans Syst Man Cybernet Part B. 2005; 35 (35): 1252-1271. (Pubitemid 41800424)
9. Ozawa S, Pang S, Kasabov N,. Incremental learning of chunk data for online pattern classification systems. IEEE Trans Neural Netw. 2008; 19 (19): 1061-1074.
10. Delany SJ, Cunningham P, Tsymbal A, Coyle L,. A case-based technique for tracking concept drift in spam filtering. Knowl-Based Syst. 2005; 18 (4-5):187-195.
11. Hulten G, Spencer L, Domingos P,. Mining time-changing data streams. In: Proc 17th Int Conf on Knowledge Discovery and Data Mining; 2001.
12. Mitra P, Murthy CA, Pal SK,. Data condensation in large databases by incremental learning with support vector machines. In: Proc 15th Int Conf on Pattern Recognition; 2000.
13. Domeniconi C, Gunopulos D,. Incremental support vector machine construction. In: Proc Int Conf on Data Mining; 2001.
14. An J-L, Wang Z-O, Ma Z-P,. Incremental learning algorithm for support vector machine. In: Proc 2nd Int Conf on Machine Learning and Cybernetics; 2003.
15. Diehl CP, Cauwenberghs G,. SVM incremental learning, adaptation and optimization. In: Proc Int Joint Conf on Neural Networks; 2003.
16. Shilton A, Palaniswami M, Ralph D, Tsoi AC,. Incremental training of support vector machines. IEEE Trans Neural Netw. 2005; 16: 114-131. (Pubitemid 40241914)
17. Vapnik VN,. The nature of statistical learning theory. New York: Springer Verlag; 1995.
18. Stanley KO,. Learning concept drift with a committee of decision trees. Tech. Rep. AI-03-302, Department of Computer Sciences, University of Texas at Austin, Austin, TX; 2003.
19. Wang H, Fan W, Yu PS, Han J,. Mining concept-drifting data streams using ensemble classifiers. In: Proc 9th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining; 2003.
20. Tsymbal A, Pechenizkiy M, Cunningham P, Puuronen S,. Dynamic integration of classifiers for handling concept drift. Inform Fusion. 2008; 9 (9): 56-68. (Pubitemid 47589061)
21. Ganti V, Gehrke J, Ramakrishnan R,. Mining data streams under block evolution. ACM SIGKDD Explor Newsl. 2002; 3 (3): 1-10.
22. Breiman L,. Pasting small votes for classification in large databases and on-line. Mach Learn. 1999; 36 (1-2):85-103.
23. Nickabadi A, Ebadzadeh MM, Safabakhsh R,. DNPSO: a dynamic niching particle swarm optimizer for multi-modal optimization. In: Proc IEEE Congress on Computational Intelligence; 2008.
24. Kapp MN, Sabourin R, Maupin P,. A PSO-based framework for dynamic svm model selection. In: Proc Genetic and Evolutionary Computation Conference; 2009.
25. Kennedy J, Eberhart RC,. Particle swarm intelligence. In: Proc Int Conf on Neural Networks; 1995.
26. Clerc M, Kennedy J,. The particle swarmâexplosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput. 2002; 6 (6): 58-73. (Pubitemid 34404564)
27. Kennedy J,. Some issues and practices for particle swarms. In: Proc IEEE Swarm Intelligence Symposium; 2007.
28. Carlisle A, Dozier G,. Tracking changing extrema with adaptive particle swarm optimizer. In: Proc 5th Biannual World Automation Congress, Orlando, FL, 2002.
29. Hu X, Eberhart RC,. Adaptive particle swarm optimization: detection and response to dynamic systems. In: Proc Congress on Evolutionary Computation; 2002.
30. Clerc M,. Particle swarm optimization. London: ISTE; 2006.
31. Chang CC, Lin CJ,. Libsvm: a library for support vector machines. Available at. Accessed October 12, 2010.
32. Breiman L,. Random forests. Mach Learn. 2001; 45 (45): 5-32. (Pubitemid 32933532)
33. Kapp MN, Sabourin R, Maupin P,. An empirical study on diversity measures and margin theory for ensembles of classifiers. In: Proc 10th Int Conf on Information Fusion; 2007.
34. Ulas A, Semerci M, Yildiz OT, Alpaydin E,. Incremental construction of classifier and discriminant ensembles. Inform Sci. 2009; 179 (179): 1298-1318.
35. Fan Wu T, Lin C-J, Weng RC,. Probability estimates for multi-class classification by pairwise coupling. J Mach Learn Res. 2003; 5: 975-1005.
36. Chapelle O, Vapnik V, Bousquet O, Mukherjee S,. Choosing multiple parameters for support vector machines. Mach Learn. 2002; 46 (1-3):131-159.