Case-based reasoning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
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
Tree induction vs. logistic regression: a learning-curve analysis
The Journal of Machine Learning Research
Predicting relative performance of classifiers from samples
ICML '05 Proceedings of the 22nd international conference on Machine learning
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
A survey of intelligent assistants for data analysis
ACM Computing Surveys (CSUR)
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This paper concerns the problem of predicting the relative performance of classification algorithms. Our approach requires that experiments are conducted on small samples. The information gathered is used to identify the nearest learning curve for which the sampling procedure was fully carried out. This allows the generation of a prediction regarding the relative performance of the algorithms. The method automatically establishes how many samples are needed and their sizes. This is done iteratively by taking into account the results of all previous experiments - both on other datasets and on the new dataset obtained so far. Experimental evaluation has shown that the method achieves better performance than previous approaches.