Abstract
One of the challenges in Machine Learning to find a classifier and parameter settings that work well on a given dataset. Evaluating all possible combinations typically takes too much time, hence many solutions have been proposed that attempt to predict which classifiers are most promising to try. As the first recommended classifier is not always the correct choice, multiple recommendations should be made, making this a ranking problem rather than a classification problem. Even though this is a well studied problem, there is currently no good way of evaluating such rankings. We advocate the use of Loss Time Curves, as used in the optimization literature. These visualize the amount of budget (time) needed to converge to a acceptable solution. We also investigate a method that utilizes the measured performances of classifiers on small samples of data to make such recommendation, and adapt it so that it works well in Loss Time space. Experimental results show that this method converges extremely fast to an acceptable solution.
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References
Abdulrahman, S.M., Brazdil, P.: Measures for combining accuracy and time for meta-learning. In: Meta-Learning and Algorithm Selection Workshop at ECAI, 2014, pp. 49–50 (2014)
Brazdil, P., Gama, J., Henery, B.: Characterizing the applicability of classification algorithms using meta-level learning. In: Bergadano, F., De Raedt, L. (eds.) ECML-94. LNCS, vol. 784, pp. 83–102. Springer, Heidelberg (1994)
Brazdil, P.B., Soares, C.: A comparison of ranking methods for classification algorithm selection. In: Lopez de Mantaras, R., Plaza, E. (eds.) ECML 2000. LNCS (LNAI), vol. 1810, pp. 63–74. Springer, Heidelberg (2000)
Fürnkranz, J., Petrak, J.: An evaluation of landmarking variants. In: Working Notes of the ECML/PKDD 2000 Workshop on Integrating Aspects of Data Mining, Decision Support and Meta-Learning, pp. 57–68 (2001)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Hutter, F., Hoos, H.H., Leyton-Brown, K., Murphy, K.: Time-bounded sequential parameter optimization. In: Blum, C., Battiti, R. (eds.) LION 4. LNCS, vol. 6073, pp. 281–298. Springer, Heidelberg (2010)
Leite, R., Brazdil, P.: Predicting relative performance of classifiers from samples. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 497–503. ACM (2005)
Leite, R., Brazdil, P.: Active testing strategy to predict the best classification algorithm via sampling and metalearning. In: ECAI, pp. 309–314 (2010)
Leite, R., Brazdil, P., Vanschoren, J.: Selecting classification algorithms with active testing. In: Perner, P. (ed.) MLDM 2012. LNCS, vol. 7376, pp. 117–131. Springer, Heidelberg (2012)
Petrak, J.: Fast subsampling performance estimates for classification algorithm selection. In: Proceedings of the ECML-00 Workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination, pp. 3–14 (2000)
Pfahringer, B., Bensusan, H., Giraud-Carrier, C.: Tell me who can learn you and i can tell you who you are: Landmarking various learning algorithms. In: Proceedings of the 17th International Conference on Machine Learning, pp. 743–750 (2000)
Provost, F., Jensen, D., Oates, T.: Efficient progressive sampling. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 23–32. ACM (1999)
Rice, J.R.: The algorithm selection problem. Adv. Comput. 15, 65118 (1976)
van Rijn, J.N., Holmes, G., Pfahringer, B., Vanschoren, J.: Algorithm selection on data streams. In: Džeroski, S., Panov, P., Kocev, D., Todorovski, L. (eds.) DS 2014. LNCS, vol. 8777, pp. 325–336. Springer, Heidelberg (2014)
Rossi, A.L.D., de Leon Ferreira, A.C.P., Soares, C., De Souza, B.F.: MetaStream: a meta-learning based method for periodic algorithm selection in time-changing data. Neurocomputing 127, 52–64 (2014)
Smith-Miles, K.A.: Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput. Surv. (CSUR) 41(1), 6 (2008)
Sun, Q., Pfahringer, B.: Pairwise meta-rules for better meta-learning-based algorithm ranking. Mach. Learn. 93(1), 141–161 (2013)
Vanschoren, J., Blockeel, H., Pfahringer, B., Holmes, G.: Experiment databases. Mach. Learn. 87(2), 127–158 (2012)
Vanschoren, J., van Rijn, J.N., Bischl, B., Torgo, L.: OpenML: networked science in machine learning. ACM SIGKDD Explor. Newsl. 15(2), 49–60 (2014)
Vilalta, R., Drissi, Y.: A perspective view and survey of meta-learning. Artif. Intell. Rev. 18(2), 77–95 (2002)
Wolpert, D.H.: Stacked generalization. Neural Networks 5(2), 241–259 (1992)
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This work is supported by grant \(600.065.120.12\mathrm {N}150\) from the Dutch Fund for Scientific Research (NWO).
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van Rijn, J.N., Abdulrahman, S.M., Brazdil, P., Vanschoren, J. (2015). Fast Algorithm Selection Using Learning Curves. In: Fromont, E., De Bie, T., van Leeuwen, M. (eds) Advances in Intelligent Data Analysis XIV. IDA 2015. Lecture Notes in Computer Science(), vol 9385. Springer, Cham. https://doi.org/10.1007/978-3-319-24465-5_26
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DOI: https://doi.org/10.1007/978-3-319-24465-5_26
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