Generalizing from case studies: a case study
ML92 Proceedings of the ninth international workshop on Machine learning
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Estimating the Predictive Accuracy of a Classifier
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Characterization of Classification Algorithms
EPIA '95 Proceedings of the 7th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Selective Sampling for Nearest Neighbor Classifiers
Machine Learning
Introduction to the Special Issue on Meta-Learning
Machine Learning
On Data and Algorithms: Understanding Inductive Performance
Machine Learning
Predicting relative performance of classifiers from samples
ICML '05 Proceedings of the 22nd international conference on Machine learning
Introduction to Linear Regression Analysis, Solutions Manual (Wiley Series in Probability and Statistics)
Selective generation of training examples in active meta-learning
International Journal of Hybrid Intelligent Systems - HIS 2007
Combining Uncertainty Sampling methods for supporting the generation of meta-examples
Information Sciences: an International Journal
Automatic selection of classification learning algorithms for data mining practitioners
Intelligent Data Analysis
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Meta-Learning predicts the performance of learning algorithms based on features of the learning problems. Meta-Learning acquires knowledge from a set of meta-examples, which store the experience obtained from applying the algorithms to problems in the past. A limitation of Meta-Learning is related to the generation of meta-examples. In order to construct a meta-example, it is necessary to empirically evaluate the algorithms on a given problem. Hence, the generation of a set of meta-examples may be costly depending on the context. In order to minimize this limitation, the use of Active Learning is proposed to reduce the number of required meta-examples. In this paper, we evaluate this proposal on a promising Meta-Learning approach, called Meta-Regression. Experiments were performed in a case study to predict the performance of learning algorithms for MLP networks. A significant performance gain was observed in the case study when Active Learning was used to support the generation of meta-examples.