×

zbMATH — the first resource for mathematics

ML-plan: automated machine learning via hierarchical planning. (English) Zbl 06990191
Summary: Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches.

MSC:
68T05 Learning and adaptive systems in artificial intelligence
PDF BibTeX Cite
Full Text: DOI
References:
[1] Bjornsson, Y; Finnsson, H, Cadiaplayer: A simulation-based general game player, IEEE Transactions on Computational Intelligence and AI in Games, 1, 4-15, (2009)
[2] Browne, C; Powley, EJ; Whitehouse, D; Lucas, SM; Cowling, PI; Rohlfshagen, P; Tavener, S; Liebana, DP; Samothrakis, S; Colton, S, A survey of Monte Carlo tree search methods, IEEE Transactions on Computational Intelligence and AI in Games, 4, 1-43, (2012)
[3] de Sá, A. G., Pinto, W. J. G., Oliveira, L. O. V., & Pappa, G. L. (2017). Recipe: A grammar-based framework for automatically evolving classification pipelines. In European Conference on Genetic Programming (pp. 246-261). Springer.
[4] Erol, K., Hendler, J. A., & Nau, D. S. (1994). UMCP: A sound and complete procedure for hierarchical task-network planning. In Proceedings of the Second International Conference on Artificial Intelligence Planning Systems, University of Chicago, Chicago, Illinois, USA, June 13-15, 1994 (pp. 249-254). http://www.aaai.org/Library/AIPS/1994/aips94-042.php.
[5] Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., & Hutter, F. (2015). Efficient and robust automated machine learning. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, & R. Garnett (Eds.), Advances in neural information processing systems (pp. 2962-2970). Curran Associates, Inc.
[6] Ghallab, M., Nau, D. S., & Traverso, P. (2004). Automated planning—Theory and practice. New York City: Elsevier. · Zbl 1074.68613
[7] Hutter, F; Hoos, HH; Leyton-Brown, K, Sequential model-based optimization for general algorithm configuration, LION, 5, 507-523, (2011)
[8] Kietz, J., Serban, F., Bernstein, A., & Fischer, S. (2009). Towards cooperative planning of data mining workflows. In Proceedings of the Third Generation Data Mining Workshop at the 2009 European Conference on Machine Learning (pp. 1-12). Citeseer.
[9] Kietz, J. U., Serban, F., Bernstein, A., & Fischer, S. (2012). Designing KDD-workflows via HTN-planning for intelligent discovery assistance. In 5th planning to learn workshop WS28 at ECAI 2012 (p. 10).
[10] Kocsis, L., Szepesvári, C., & Willemson, J. (2006). Improved Monte-Carlo search. Technical report 1, University of Tartu, Estonia.
[11] Komer, B., Bergstra, J., & Eliasmith, C. (2014). Hyperopt-sklearn: Automatic hyperparameter configuration for scikit-learn. In ICML workshop on AutoML.
[12] Kotthoff, L; Thornton, C; Hoos, HH; Hutter, F; Leyton-Brown, K, Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA, The Journal of Machine Learning Research, 18, 826-830, (2017) · Zbl 06781346
[13] Lloyd, J. R., Duvenaud, D. K., Grosse, R. B., Tenenbaum, J. B., & Ghahramani, Z. (2014). Automatic construction and natural-language description of nonparametric regression models. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, Québec City, Québec, Canada (pp. 1242-1250).
[14] Mohr, F., Wever, M., Hüllermeier, E., & Faez, A. (2018). Towards the automated composition of machine learning services. In Proceedings of the IEEE International Conference on Services Computing. SCC.
[15] Nau, DS; Au, T; Ilghami, O; Kuter, U; Murdock, JW; Wu, D; Yaman, F, SHOP2: an HTN planning system, Journal of Artificial Intelligence Research (JAIR), 20, 379-404, (2003) · Zbl 1058.68106
[16] Nguyen, P; Hilario, M; Kalousis, A, Using meta-mining to support data mining workflow planning and optimization, Journal of Artificial Intelligence Research, 51, 605-644, (2014)
[17] Nguyen, P., Kalousis, A., & Hilario, M. (2011). A meta-mining infrastructure to support KD workflow optimization. In Proceedings of the PlanSoKD-11 Workshop at ECML/PKDD (pp. 1-10).
[18] Nguyen, P., Kalousis, A., & Hilario, M. (2012). Experimental evaluation of the e-lico meta-miner. In 5th planning to learn workshop WS28 at ECAI (pp. 18-19).
[19] Olson, R. S., & Moore, J. H. (2016). Tpot: A tree-based pipeline optimization tool for automating machine learning. In Workshop on automatic machine learning (pp. 66-74).
[20] Schadd, MPD; Winands, MHM; Herik, HJ; Chaslot, GMJB; Uiterwijk, JWHM; Herik, HJ (ed.); Xu, X (ed.); Ma, Z (ed.); Winands, MHM (ed.), Single-player Monte-Carlo tree search, (2008), Berlin
[21] Thornton, C., Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2013). Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, IL, USA (pp. 847-855).
[22] Vanschoren, J; Rijn, JN; Bischl, B; Torgo, L, Openml: networked science in machine learning, SIGKDD explorations, 15, 49-60, (2013)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.