Abstract
The paper presents a framework which brings together the tools necessary to analyze new problems and make predictions related to the learning algorithms’ performance and automate the analyst’s work. We focus on minimizing the system dependence on user input while still providing the ability of a guided search for a suitable learning algorithm through performance metrics. Predictions are computed using different strategies for calculating the distance between datasets, selecting neighbors and combining existing results. The framework is available for free use on the internet.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aha, D.W.: Generalizing from Case Studies: A Case Study. In: Proceedings of the Ninth International Conference on Machine Learning, pp. 1–10 (1992)
Bensusan, H., Giraud-Carrier, C., Kennedy, C.J.: A Higher-Order Approach to Meta-Learning. In: Proceedings of the ECML 2000 Workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination, pp. 33–42 (2000)
Brazdil, P.B., Soares, C., da Costa, J.P.: Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results. Machine Learning 50, 251–277 (2003)
Cacoveanu, S., Vidrighin, C., Potolea, R.: Evolutional Meta-Learning Framework for Automatic Classifier Selection. In: Proceedings of the 5th International Conference on Intelligent Computer Communication and Processing, Cluj-Napoca, pp. 27–30 (2009)
Caruana, R., Niculescu-Mizil, A.: Data Mining in Metric Space: An Empirical Analysis of Supervised Learning Performance Criteria. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 69–78 (2004)
Giraud-Carrier, C., Bensusan, H.: Discovering Task Neighbourhoods Through Landmark Learning Performances. In: Proceedings of the Fourth European Conference of Principles and Practice of Knowledge Discovery in Databases, pp. 325–330 (2000)
Japkowicz, N., Stephen, S.: The Class Imbalance Problem: A Systematic Study. Intelligent Data Analysis 6(5), 429–450 (2002)
Kalousis, A.: Algorithm Selection via Meta_Learning, PhD Thesis, Faculte des sciences de l’Universite de Geneve (2002)
Linder, C., Studer, R.: Support for Algorithm Selection with a CBR Approach. In: Proceedings of the 16th International Conference on Machine Learning, pp. 418–423 (1999)
Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine Learning. Neural and Statistical Classification. Ellis Horwood Series in Artificial Intelligence (1994)
Niculescu-Mizil, A., et al.: Winning the KDD Cup Orange Challenge with Ensemble Selection. In: JMLR Workshop and Conference Proceedings 7 (2009)
Rendel, L., Seshu, R., Tcheng, D.: Layered concept learning and dynamically variable bias management. In: 10th Internatinal Joint Conf. on AI, pp. 308–314 (1987)
Schaffner, C.: Selecting a classification method by cross validation. Machine Learning 13, 135–143 (1993)
Sleeman, D., Rissakis, M., Craw, S., Graner, N., Sharma, S.: Consultant-2: Pre and post-processing of machine learning applications. International Journal of Human Computer Studies, 43–63 (1995)
UCI Machine Learning Data Repository, http://archive.ics.uci.edu/ml/ (last accessed January 2010)
Vilalta, R., Giraud-Carrier, C., Brazdil, P., Soares, C.: Using Meta-Learning to Support Data Mining. International Journal of Computer Science & Applications, 31–45 (2004)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann Publishers, Elsevier Inc. (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Potolea, R., Cacoveanu, S., Lemnaru, C. (2011). Meta-learning Framework for Prediction Strategy Evaluation. In: Filipe, J., Cordeiro, J. (eds) Enterprise Information Systems. ICEIS 2010. Lecture Notes in Business Information Processing, vol 73. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19802-1_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-19802-1_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19801-4
Online ISBN: 978-3-642-19802-1
eBook Packages: Computer ScienceComputer Science (R0)