A metric for unsupervised metalearning

Intelligent Data Analysis

Volumn 15, Issue 6, 2011, Pages 827-841

  Lee, Jun Won ^a   Giraud Carrier, Christophe ^a  

^a 3361 TMCB   (United States)

Author keywords

behavior distance; clustering; Unsupervised metalearning

Indexed keywords

BEHAVIOR DISTANCE; CLUSTERING; DISTANCE FUNCTIONS; ENSEMBLE LEARNING; METALEARNING;

LEARNING ALGORITHMS;

CLUSTERING ALGORITHMS;

EID: 84863036476     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/IDA-2011-0498     Document Type: Article

References (36)

1. K. Ali and M. Pazzani, Error reduction through learning multiple descriptions, Machine Learning 24(3) (1996), 173-202.
2. S. Ali and K.A. Smith, On learning algorithm selection for classification, Applied Soft Computing 6(2) (2006), 119-138.
3. A.Asuncion and D.J. Newman, UCI machine learning repository [http://archive.ics.uci.edu/ml/], University of California, Irvine, School of Information and Computer Sciences, 2007.
4. K. Bailey, Methods of social research, fourth edition, Simon and Schuster, 2007.
5. P. Brazdil, C. Giraud-Carrier, C. Soares and R. Vilalta, Metalearning: Applications to Data Mining, Springer, 2009.
6. P. Brazdil, C. Soares and J. Pinto da Costa, Ranking learning algorithms: Using ibl and meta-learning on accuracy and time results, Machine Learning 50(3) (2003), 251-277.
7. C.E. Brodley and T. Lane, Creating and exploiting coverage and diversity, In Proceedings of the AAAI-96 Workshop Integrating Multiple Learned Models, pages 8-14, 1996.
8. G. Brown, J.Wyatt, R. Harris and X. Yao, Diversity creation methods: A survey and categorisation, Information Fusion 6(1) (2005), 5-20.
9. R. Caruana, A. Niculescu-Mizil, G. Crew and A. Ksikes, Ensemble selection from libraries of models, In Proceedings of the Twenty-first International Conference on Machine Learning, 2004.
10. Y-S Chung, D.F. Hsu and C.Y. Tang, On the relationships among various diversity measures in multiple classifier systems, In International Symposium on Parallel Architectures, Algorithms and Networks, pages 184-190, 2008.
11. T.G. Dietterich, An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization, Machine Learning 40(2) (2000), 139-157.
12. J. Fleiss, Statistical Methods for Rates and Proportions, John Wiley & Sons, 1981.
13. E. Gatnar, A diversity measure for tree-based classifier ensembles, in: Data Analysis and Decision Support, D. Baier, R. Decker and L. Schmidt-Thieme, eds, Springer Berlin Heidelberg, 2005, pp. 30-38.
14. G. Giacinto and F. Roli, Design of effective neural network ensembles for image classification processes, Journal of Image Vision and Computing 19 (2001), 600-707.
15. R.C. Holte, Very simple classification rules perform well on most commonly used datasets, Machine Learning 11(1) (1993), 63-91.
16. A. Kalousis, J. Gama and M. Hilario, On data and algorithms: Understanding inductive performance, Machine Learning 54(3) (2004), 275-312.
17. L.I. Kuncheva and C.J. Whitaker, Ten measures of diversity in classifier ensembles: Limits for two classifiers, In Proceedings of the IEE Workshop on Intelligent Sensor Processing, pages 1-10, 2001.
18. L.I. Kuncheva and C.J. Whitaker, Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy, Machine Learning 51(2) (2003), 181-207.
19. J. Lee and C. Giraud-Carrier, New insights into learning algorithms and datasets, In Proceedings of the Seventh International Conference on Machine Learning and Applications, pages 135-140, 2008.
20. J. Lee and C. Giraud-Carrier, Clustering-based metalearning for algorithm selection, In preparation, 2010.
21. J. Lee and C. Giraud-Carrier, A comparison of naive bayes and radial basis function networks. In preparation, 2010.
22. A. Narasimhamurthy, Evaluation of diversity measures for binary classifier ensembles, In Proceedings of the Sixth International Workshop on Multiple Classifier Systems (LNCS 3541), pages 267-277, 2005.
23. A.H. Peterson and T.R. Martinez, Estimating the potential for combining learning models, In Proceedings of the ICML Workshop on Meta-Learning, pages 68-75, 2005.
24. B. Pfahringer, H. Bensusan and C. Giraud-Carrier, Meta-learning by landmarking various learning algorithms, In Proceedings of the Seventeenth International Conference on Machine Learning (ICML-2000), pages 743-750, 2000.
25. R.B.C. Prudêncio and T.B. Ludermir, Active selection of training examples for meta-learning, In Proceedings of the Seventh International Conference on Hybrid Intelligent Systems, pages 126-131, 2007.
26. R. Development Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2007. ISBN 3-900051-07-0.
27. M. Sirkin, Statistics for the Social Sciences, SAGE, 2006.
28. David B. Skalak, The sources of increased accuracy for two proposed boosting algorithms, In In Proc. American Association for Arti Intelligence, AAAI-96, Integrating Multiple Learned Models Workshop, pages 120-125, 1996.
29. K.A. Smith, F. Woo, V. Ciesielski and R. Ibrahim, Matching data mining algorithm suitability to data characteristics using a self-organizing map, In Proceedings of the First International Workshop on Hybrid Intelligent Systems, pages 169-179, 2001.
30. K.A. Smith-Miles, Cross-disciplinary perspectives on meta-learning for algorithm selection, ACM Computing Surveys, 2008.
31. G. Stiglic and P.Kokol, GEMLeR: Gene expression machine learning repository [http://gemler.fzv.uni-mb.si/]. University of Maribor, Faculty of Health Sciences, 2009.
32. E.K. Tang, P.N. Suganthan and X. Yao, An analysis of diversity measures, Machine Learning 65(1) (2006), 247-271.
33. X. Wang, K. Smith and R. Hyndman, Characteristic-based clustering for time series data, Data Mining and Knowledge Discovery 13(3) (2006), 335-364.
34. I.H.Witten and F. Eibe, Data Mining: Practical Machine Learning Tools with Java Implementations, Morgan Kaufmann, San Francisco, CA, 2000.
35. D.H. Wolpert, Any two learning algorithms are (almost) exactly identical, Technical report, NASA Ames Research Center, 2001.
36. G. Yule, On the association of attributes in statistics, Philosophical Transactions of the Royal Society of London, Ser. A 194 (1900), 257-319.