Selecting classification algorithms with active testing

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7376 LNAI, Issue , 2012, Pages 117-131

  Leite, Rui ^a   Brazdil, Pavel ^a   Vanschoren, Joaquin ^b  

^a UNIVERSITY OF PORTO   (Portugal)

^b LEIDEN UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ALGORITHM; CROSS-VALIDATION TESTS; DATA MINING ALGORITHM; DATA SETS; NOVEL TECHNIQUES; PARAMETER SETTING;

ALGORITHMS; CLASSIFICATION (OF INFORMATION); LEARNING SYSTEMS; PATTERN RECOGNITION; STATISTICAL TESTS;

DATA MINING;

EID: 84864928603     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-31537-4_10     Document Type: Conference Paper

References (23)

1. Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998)
2. Pfahringer, B., Bensussan, H., Giraud-Carrier, C.: Meta-learning by landmarking various learning algorithms. In: Proceedings of the 17th Int. Conf. on Machine Learning (ICML 2000), Stanford, CA (2000)
3. Brazdil, P., Soares, C., Costa, J.: Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results. Machine Learning 50, 251-277 (2003)
4. De Grave, K., Ramon, J., De Raedt, L.: Active learning for primary drug screening. In: Proceedings of Discovery Science. Springer (2008)
5. Demsar, J.: Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research 7, 1-30 (2006)
6. Fedorov, V.: Theory of Optimal Experiments. Academic Press, New York (1972)
7. Freund, Y., Seung, H., Shamir, E., Tishby, N.: Selective sampling using the query by committee algorithm. Machine Learning 28, 133-168 (1997)
8. Fürnkranz, J., Petrak, J.: An evaluation of landmarking variants. In: Proceedings of the ECML/PKDD Workshop on Integrating Aspects of Data Mining, Decision Support and Meta-Learning (IDDM 2001), pp. 57-68. Springer (2001)
9. Gittins, J.: Multi-armed bandit allocation indices. Wiley Interscience Series in Systems and Optimization. John Wiley & Sons, Ltd. (1989)
10. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10-18 (2009)
11. Blockeel, H.: Experiment Databases: A Novel Methodology for Experimental Research. In: Bonchi, F., Boulicaut, J.-F. (eds.) KDID 2005. LNCS, vol. 3933, pp. 72-85. Springer, Heidelberg (2006)
12. Fürnkranz, J., Petrak, J.: An evaluation of landmarking variants. In: Carrier, C., Lavrac, N., Moyle, S. (eds.) Working Notes of ECML/PKDD 2000 Workshop on Integration Aspects of Data Mining, Decision Support and Meta-Learning (2001)
13. Leite, R., Brazdil, P.: Predicting relative performance of classifiers from samples. In: ICML 2005: Proceedings of the 22nd International Conference on Machine Learning, pp. 497-503. ACM Press, New York (2005)
14. Leite, R., Brazdil, P.: Active testing strategy to predict the best classification algorithm via sampling and metalearning. In: Proceedings of the 19th European Conference on Artificial Intelligence, ECAI 2010 (2010)
15. Long, B., Chapelle, O., Zhang, Y., Chang, Y., Zheng, Z., Tseng, B.: Active learning for rankings through expected loss optimization. In: Proceedings of the SIGIR 2010. ACM (2010)
16. Mahajan, A., Teneketzis, D.: Multi-armed bandit problems. In: Castanon, D.A., Cochran, D., Kastella, K. (eds.) Foundations and Applications of Sensor Management. Springer (2007)
17. Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine Learning, Neural and Statistical Classification. Ellis Horwood (1994)
18. Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
19. Rice, J.R.: The algorithm selection problem. Advances in Computers, vol. 15, pp. 65-118. Elsevier (1976)
20. Smith-Miles, K.A.: Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput. Surv. 41(1), 1-25 (2008)
21. Soares, C., Petrak, J., Brazdil, P.: Sampling-Based Relative Landmarks: Systematically Test-Driving Algorithms before Choosing. In: Brazdil, P.B., Jorge, A.M. (eds.) EPIA 2001. LNCS (LNAI), vol. 2258, pp. 88-94. Springer, Heidelberg (2001)
22. Vanschoren, J., Blockeel, H.: A Community-Based Platform for Machine Learning Experimentation. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5782, pp. 750-754. Springer, Heidelberg (2009)
23. Vilalta, R., Drissi, Y.: A perspective view and survey of meta-learning. Artif. Intell. Rev. 18(2), 77-95 (2002)