D. W. Aha, Generalizing from Case Studies: A Case Study, Proceedings of the Ninth International Conference on Machine Learning, pp.1-10, 1992.
DOI : 10.1016/B978-1-55860-247-2.50006-1

S. Ali and K. A. Smith-miles, A meta-learning approach to automatic kernel selection for support vector machines, Neural Networks Selected Papers from the 7th Brazilian Symposium on Neural Networks (SBRN '04) 7th Brazilian Symposium on Neural Networks, pp.173-186, 2006.
DOI : 10.1016/j.neucom.2006.03.004

N. Bhatt, A. Thakkar, and A. Ganatra, « A survey and current research challenges in meta learning approaches based on dataset characteristics, International Journal of Soft Computing and Engineering, vol.2, issue.10, pp.234-247, 2012.

N. Bhatt, A. Thakkar, A. Ganatra, and N. Bhatt, Ranking of Classifiers based on Dataset Characteristics using Active Meta Learning, International Journal of Computer Applications, vol.69, issue.20, pp.31-36, 2013.
DOI : 10.5120/12089-8269

P. B. Brazdil, C. Soares, D. Costa, and J. P. , « Ranking Learning Algorithms Using IBL and Meta- Learning on Accuracy and Time Results, Machine Learning, vol.50, issue.3, pp.251-277, 2003.
DOI : 10.1023/A:1021713901879

K. Furdík, J. Parali?, and G. Tutoky, learning method for automatic selection of algorithms for text classification, Proc. of the Central European Conference on Information and Intelligent Systems, pp.24-26, 2008.

A. Kalousis, J. A. Gama, and M. Hilario, On Data and Algorithms: Understanding Inductive Performance, Machine Learning, vol.54, issue.3, pp.275-312, 2004.
DOI : 10.1023/B:MACH.0000015882.38031.85

W. Lam, K. Lai, and . Meta, A meta-learning approach for text categorization, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '01, pp.303-309, 2001.
DOI : 10.1145/383952.384011

R. Leite and P. Brazdil, Active Testing Strategy to Predict the Best Classification Algorithm via Sampling and Metalearning The Netherlands, The Netherlands, Proceedings of the 2010 Conference on ECAI 2010 19th European Conference on Artificial Intelligence, pp.309-314, 2010.

Y. Lin, J. Jiang, and S. Lee, A Similarity Measure for Text Classification and Clustering, IEEE Transactions on Knowledge and Data Engineering, vol.26, issue.7, p.1, 2013.
DOI : 10.1109/TKDE.2013.19

R. Pavón, F. Díaz, R. Laza, and V. Luzón, Automatic parameter tuning with a Bayesian case-based reasoning system. A case of study, Expert Systems with Applications, vol.36, issue.2, pp.3407-3420, 2009.
DOI : 10.1016/j.eswa.2008.02.044

Y. Peng, P. A. Flach, C. Soares, and P. Brazdil, « Improved Dataset Characterisation for Metalearning, Proceedings of the 5th International Conference on Discovery Science, DS '02, pp.141-152, 2002.

B. Pfahringer, H. Bensusan, and C. G. Giraud-carrier, Learning by Landmarking Various Learning Algorithms, Proceedings of the Seventeenth International Conference on Machine Learning, ICML '00, pp.743-750, 2000.

M. Reif, F. Shafait, and A. Dengel, Prediction of Classifier Training Time Including Parameter Optimization, Proceedings of the 34th Annual German Conference on Advances in Artificial Intelligence, pp.260-271, 2011.
DOI : 10.1007/978-3-642-24455-1_25

M. Reif, F. Shafait, and A. Dengel, Meta-learning for evolutionary parameter optimization of classifiers, Machine Learning, vol.4, issue.1, pp.357-380, 2012.
DOI : 10.1007/s10994-012-5286-7

J. R. Rice, The Algorithm Selection Problem, Advances in Computers, pp.65-118, 1976.
DOI : 10.1016/S0065-2458(08)60520-3

K. A. Smith-miles, Cross-disciplinary perspectives on meta-learning for algorithm selection, ACM Computing Surveys, vol.41, issue.1, pp.61-625, 2009.
DOI : 10.1145/1456650.1456656

Q. Sun and B. Pfahringer, Pairwise meta-rules for better meta-learning-based algorithm ranking, Machine Learning, pp.141-161, 2013.
DOI : 10.1007/s10994-013-5387-y

D. H. Wolpert and W. G. Macready, « No free lunch theorems for optimization », Evolutionary Computation, IEEE Transactions on, vol.1, issue.1, pp.67-82, 1997.
DOI : 10.1109/4235.585893
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.138.6606