Case-based reasoning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Efficient progressive sampling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Selective generation of training examples in active meta-learning
International Journal of Hybrid Intelligent Systems - HIS 2007
Predicting the Performance of Learning Algorithms Using Support Vector Machines as Meta-regressors
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Active Generation of Training Examples in Meta-Regression
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Active learning to support the generation of meta-examples
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
An iterative process for building learning curves and predicting relative performance of classifiers
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Active Testing Strategy to Predict the Best Classification Algorithm via Sampling and Metalearning
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Combining Uncertainty Sampling methods for supporting the generation of meta-examples
Information Sciences: an International Journal
Selecting classification algorithms with active testing
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Pairwise meta-rules for better meta-learning-based algorithm ranking
Machine Learning
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This paper is concerned with the problem of predicting relative performance of classification algorithms. It focusses on methods that use results on small samples and discusses the shortcomings of previous approaches. A new variant is proposed that exploits, as some previous approaches, meta-learning. The method requires that experiments be conducted on few samples. The information gathered is used to identify the nearest learning curve for which the sampling procedure was carried out fully. This in turn permits to generate a prediction regards the relative performance of algorithms. Experimental evaluation shows that the method competes well with previous approaches and provides quite good and practical solution to this problem.