Pairwise meta-rules for better meta-learning-based algorithm ranking

Machine Learning

Volumn 93, Issue 1, 2013, Pages 141-161

  Sun, Quan ^a   Pfahringer, Bernhard ^a  

^a UNIVERSITY OF WAIKATO   (New Zealand)

Author keywords

Algorithm ranking; Ensemble learning; Meta learning; Ranking trees

Indexed keywords

BASE LEARNERS; ENSEMBLE LEARNING; GENERATION METHOD; META-FEATURES; META-LEARNER; METAFEATURE; METALEARNING; RANKING TREES;

FORESTRY;

LEARNING ALGORITHMS;

ALGORITHMS; FORESTRY;

EID: 84882265559     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-013-5387-y     Document Type: Article

References (43)

1. Ali, S., & Smith-Miles, K. A. (2006). A meta-learning approach to automatic kernel selection for support vector machines. Neurocomputing, 70(1-3), 173-186.
2. Alvo, M., Cabilio, P., & Feigin, P. D. (1982). Asymptotic theory for measures of concordance with special reference to average Kendall tau. The Annals of Statistics, 10(4), 1269-1276.
3. Bensusan, H., Giraud-Carrier, C., & Kennedy, C. (2000). A higher-order approach to meta-learning (Technical report). University of Bristol.
4. Biau, G. (2012). Analysis of a random forests model. Journal of Machine Learning Research, 13, 1063-1095.
5. Blockeel, H., Raedt, L. D., & Ramon, J. (1998). Top-down induction of clustering trees. In Proceedings of the fifteenth international conference on machine learning. San Mateo: Morgan Kaufmann.
6. Brazdil, P., Gama, J., & Henery, B. (1994). Characterizing the applicability of classification algorithms using meta-level learning. In Proceedings of the European conference on machine learning.
7. Brazdil, P., Soares, C., & Da Costa, J. P. (2003). Ranking learning algorithms: using ibl and meta-learning on accuracy and time results. Machine Learning, 50(3), 251-277.
8. Brazdil, P., Giraud-Carrier, C., Soares, C., & Vilalta, R. (2009). Metalearning: applications to data mining. Berlin: Springer.
9. Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
10. Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32.
11. Cheng, W., & Hüllermeier, E. (2008). Instance-based label ranking using the mallows model. In Workshop proceedings of preference learning, Antwerp, Belgium.
12. Cheng, W., Hühn, J., & Hüllermeier, E. (2009). Decision tree and instance-based learning for label ranking. In Proceedings of the 26th international conference on machine learning (ICML-09), Montreal, Canada (pp. 161-168).
13. Cohen, W. W. (1995). Fast effective rule induction. In Proceedings of the 12th international conference on machine learning. San Mateo: Morgan Kaufmann.
14. de Miranda, P., Prudencio, R., Carvalho, A., & Soares, C. (2012). Combining a multi-objective optimization approach with meta-learning for svm parameter selection. In 2012 IEEE international conference on systems, man, and cybernetics (pp. 2909-2914).
15. Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. London: Chapman & Hall.
16. Escalante, H. J., Montes, M., & Sucar, L. E. (2009). Particle swarm model selection. Journal of Machine Learning Research, 10, 405-440.
17. Giraud-Carrier, C. (2008). Metalearning - a tutorial. In Proceedings of the 7th international conference on machine learning and applications. San Mateo: Morgan Kaufmann.
18. Gomes, T. A., Prudêncio, R. B., Soares, C., Rossi, A. L., & Carvalho, A. (2012). Combining meta-learning and search techniques to select parameters for support vector machines. Neurocomputing, 75(1), 3-13.
19. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The weka data mining software: an update. ACM SIGKDD Explorations Newsletter, 11(1), 10-18.
20. Hüllermeier, E., Fürnkranz, J., Cheng, W., & Brinker, K. (2008). Label ranking by learning pairwise preferences. Artificial Intelligence, 172(16-17), 1897-1916.
21. Jankowski, N., Duch, W., & Grabczewski, K. (Eds.) (2011). Studies in computational intelligence: Vol. 358. Meta-learning in computational intelligence. Berlin: Springer.
22. Järvelin, K., & Kekäläinen, J. (2002). Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems, 20(4), 422-446.
23. Kalousis, A. (2002). Algorithm selection via meta-learning. PhD thesis, Department of Computer Science, University of Geneva.
24. Kalousis, A., & Hilario, M. (2001). Model selection via meta-learning: a comparative study. International Journal on Artificial Intelligence Tools, 10(04), 525-554.
25. Kendall, M. G. (1970). Rank correlation methods. London: Griffin.
26. Leite, R., & Brazdil, P. (2005). Predicting relative performance of classifiers from samples. In Proceedings of the 22nd international conference on machine learning.
27. Leite, R., Brazdil, P., & Vanschoren, J. (2012a). Selecting classification algorithm with active testing on similar datasets. In Proceedings of the 5th international workshop on planning to learn.
28. Leite, R., Brazdil, P., & Vanschoren, J. (2012b). Selecting classification algorithms with active testing. In P. Perner (Ed.), Lecture notes in computer science: Vol. 7376. Machine learning and data mining in pattern recognition (pp. 117-131). Berlin Heidelberg: Springer.
29. Li, H. (2011). Learning to rank for information retrieval and natural language processing. Synthesis Lectures on Human Language Technologies, 4(1), 1-113.
30. Marden, J. I. (1995). Analyzing and modeling rank data. London: Chapman & Hall.
31. Pfahringer, B., Bensusan, H., & Giraud-Carrier, C. (2000). Meta-learning by landmarking various learning algorithms. In Proceedings of the 17th international conference on machine learning.
32. Pinto da Costa, J., & Soares, C. (2005). A weighted rank measure of correlation. Australian & New Zealand Journal of Statistics, 47(4), 515-529.
33. Reif, M., Shafait, F., & Dengel, A. (2012). Meta-learning for evolutionary parameter optimization of classifiers. Machine Learning, 87, 357-380.
34. Schaffer, C. (1994). A conservation law for generalization performance. In Proceedings of the 11th international conference on machine learning (pp. 259-265). San Mateo: Morgan Kaufmann.
35. Serban, F., Vanschoren, J., Kietz, J.-U., & Bernstein, A. (2013). A survey of intelligent assistants for data analysis. ACM Computing Surveys. doi: 10.5167/uzh-73010.
36. Smith-Miles, K. A. (2009). Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Computing Surveys, 41(1), 6:1-6:25.
37. Soares, C. (2004). Learning ranking of learning algorithms. PhD thesis, Department of Computer Science, University of Porto.
38. Soares, C., Brazdil, P. B., & Kuba, P. (2004). A meta-learning method to select the kernel width in support vector regression. Machine Learning, 54(3), 195-209.
39. Sun, Q., Pfahringer, B., & Mayo, M. (2012). Full model selection in the space of data mining operators. In Proceedings of the 14th international conference on genetic and evolutionary computation conference companion.
40. Todorovski, L., Blockeel, H., & Dzeroski, S. (2002). Ranking with predictive clustering trees. In Proceedings of the 13th European conference on machine learning. Berlin: Springer.
41. Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5, 241-259.
42. Wolpert, D., & Macready, W. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67-82.
43. Xu, J., & Li, H. (2007). Adarank: a boosting algorithm for information retrieval. In Proceedings of the 30th international conference on research and development in information retrieval. New York: ACM.