Adaptation in classification systems

Studies in Computational Intelligence

Volumn 202, Issue , 2009, Pages 237-258

  Bouchachia, Abdelhamid ^a  

^a UNIVERSITY OF KLAGENFURT   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 66749118325     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-01533-5_9     Document Type: Article

References (45)

1. Battiti, R., Colla, A.: Democracy in neural nets: Voting schemes for classification. Neural Networks 7(4), 691-707 (1994)
2. Blum, A.: Empirical support for winnow and weighted-majority based algorithms: Results on a calendar scheduling domain. Machine Learning 26, 5-23 (1997)
3. Bouchachia, A.: Incremental learning via function decomposition. In: Proc. of the Int. Conf. on machine learning and applications, pp. 63-68 (2006)
4. Bouchachia, A.: Incremental Learning. In: Encyclopedia of Data Warehousing and Mining, 2nd edn., Idea-Group (in press) (2008)
5. Breiman, L.: Bagging predictors. Machine Learning 24(2), 123-140 (1996)
6. Breiman, L.: Pasting small votes for classification in large databases and on-line. Machine Learning 36, 85-103 (1999)
7. Carpenter, G., Grossberg, S., Rosen, D.: Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System. Neural Networks 4(6), 759-771 (1991)
8. Dietrich, C., Palm, G., Schwenker, F.: Decision templates for the classification of bioacoustic time series. Information Fusion 4(2), 101-109 (2003)
9. Fern, A., Givan, R.: Machine learning. Machine Learning 53, 71-109 (2003)
10. French, R.: Catastrophic forgetting in connectionist networks: Causes, consequences and solutions. Trends in Cognitive Sciences, Trends in Cognitive Sciences 3(4), 128-135 (1999)
11. Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings of the Second European Conference on Computational Learning Theory, pp. 23-37 (1995)
12. Fritzke, B.: A growing neural gas network learns topologies. In: Advances in neural information processing systems, pp. 625-632 (1995)
13. Gabrys, B., Bargiela, A.: General fuzzy min-max neural network for clustering and classification. IEEE Trans. on Neural Networks 11(3), 769-783 (2000)
14. Grossberg, S.: NonlinPar neural networks: principles, mechanism, and architectures. Neural Networks 1, 17-61 (1988)
15. Hettich, S., Blake, C., Merz, C.: UCI repository of machine learning databases (1998), www.ics.uci.edu/-mlearn/MLRepository.html
16. Huang, Y., Suen, C.: A method of combining multiple experts for the recognition of unconstrained handwritten numerals. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(1), 90-94 (1995)
17. Jacobs, R.: Methods of combining experts' probability assessments. Neural Computing 7, 865-888 (1995)
18. Jacobs, R., Jordan, M., Nowlan, S., Hinton, G.: Adaptive mixtures of local experts. Neural Computing 3, 79-87 (1991)
19. Stanley, K.: Learning concept drift with a committee of decision trees. Technical Report TR-03-302, Dept of Computer Science, University of Texas at Austin, USA (2003)
20. Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226-239 (1998)
21. Klinkenberg, R.: LearnEng drifting concepts: example selection vs. example weighting. Intelligent Data Analysis 8(3), 281-300 (2004)
22. Kolter, J., Maloof, MA: Dynamic weighted majority: a new ensemble method for tracking concept drift. In: Proceedings of the 3rd International Conference on Data Mining ICDM 2003, pp. 123-130. IEEE CS Press, Los Alamitos (2003)
23. Kuncheva, L.: A theoretical study on six classifier fusion strategies. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(2), 281-286 (2002)
24. Kuncheva, L.: Classifier ensembles for changing environments. In: Proc. of the 5th international workshop on multiple classifier systems, pp. 1-15 (2004)
25. Kuncheva, L., Bezdek, J., Duin, R.: Decision templates for multiple classifier fusion: An experimental comparison. Pattern Recognition 34(2), 299-314 (2001)
26. Lam, L., Suen, C.: Optimal combinations of pattern classifiers. Pattern Recognition Letters 16, 945-954 (1995)
27. Littlestone, N.: Learning quickly when irrelevant attributes abound: A new linear threshold algorithm. Machine Learning 2, 285-318 (1988)
28. Martinetz, T., Berkovich, S., Schulten, K.: Neural gas network for vector quantization and its application to time-series prediction. IEEE Trans. Neural Networks 4(4), 558-569 (1993)
29. McCloskey, M., Cohen, N.: Catastrophic interference in connectionist networks: The sequential learning problem. The psychology of learning and motivation 24, 109-164 (1999)
30. Oza, N.: Online Ensemble Learning. Phd thesis, University of California, Berkeley (2001)
31. Promise. Software engineering repository (May 2008), http://promise.site.uottawa.ca/SERepository
32. Quartz, S., Sejnowski, T.: The neuralAbasis of cognitive development: a constructivist manifesto. Behavioral and Brain Sciences 20(4), 537-556 (1997)
33. Ratcliff, R.: Connectionist models of Cecognition memory: constraints imposed by learning and forgetting functions. Psychological Review 97, 285-308 (1990)
34. Sahel, Z., Bouchachia, A., Gabrys, B.: Adaptive mechanisms for classification problems with drifting data. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part II. LNCS, vol. 4693, pp. 419-426. Springer, Heidelberg (2007)
35. Salzberg, S.: A nearest hyperrectangle learning method. Machine learning 6, 277-309 (1991)
36. Sharkey, N., Sharkey, A.: Catastrophic forgetting in connectionist networks: Causes, consequences and solutions. An analysis of catastrophic interference 7(3-4), 301-329 (1995)
37. Sirosh, J., Miikkulainen, R., Choe, Y. (eds.): Lateral Interactions in the Cortex: Structure and Function, The UTCS Neural Networks Research Group, Austin, TX, Electronic book (1996)
38. Slaganicoff, M.: Density-adaptive learning and forgetting. In: Proc. of the 10th Int. Conf. on Machine Learning, pp. 276-283 (1993)
39. Street, W., Kim, Y.: A streaming ensemble algorithm (sea) for large-scale classification. In: Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining KDDM 2001, pp. 377-382 (2001)
40. Tax, D., van Breukelen, M., Duin, R., Kittler, J.: Combining multiple classifiers by averaging or by multiplying? Pattern Recognition 33(9), 1475-1485 (2000)
41. Tsymbal, A., Pechenizkiy, M., Cunningham, P., Puuronen, S.: Handling local concept drift with dynamic integration of classifiers: Domain of antibiotic resistance in nosocomial infections. In: Proc. of the 19th IEEE Symposium on Computer-Based Medical Systems, pp. 679-684 (2006)
42. Wettschereck, D., Dietterich, T.: An experimental comparison of the nearest-neighbor and nearest-hyperrectangle algorithms. Machine Learning 19(1), 5-27 (1995)
43. Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23, 69-101 (1996)
44. Wolpert, D.: Stacked generalization. Neural Networks 5(2), 241-259 (1992)
45. Woods, K., Kegelmeyer, W., Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 405-410 (1997)