C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Meta Analysis of Classification Algorithms for Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
God Doesn't Always Shave with Occam's Razor - Learning When and How to Prune
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Characterization of Classification Algorithms
EPIA '95 Proceedings of the 7th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The lack of a priori distinctions between learning algorithms
Neural Computation
The existence of a priori distinctions between learning algorithms
Neural Computation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Ranking with Predictive Clustering Trees
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Sampling-Based Relative Landmarks: Systematically Test-Driving Algorithms Before Choosing
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
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
Estimating accuracy for text classification tasks on large unlabeled data
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Prediction of classifier training time including parameter optimization
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Combining Uncertainty Sampling methods for supporting the generation of meta-examples
Information Sciences: an International Journal
Using genetic algorithms to improve prediction of execution times of ML tasks
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
A survey of intelligent assistants for data analysis
ACM Computing Surveys (CSUR)
Efficient feature size reduction via predictive forward selection
Pattern Recognition
Predicting execution time of machine learning tasks for scheduling
International Journal of Hybrid Intelligent Systems
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This paper investigates the use of meta-learning to estimate the predictive accuracy of a classifier. We present a scenario where meta-learning is seen as a regression task and consider its potential in connection with three strategies of dataset characterization. We show that it is possible to estimate classifier performance with a high degree of confidence and gain knowledge about the classifier through the regression models generated. We exploit the results of the models to predict the ranking of the inducers. We also show that the best strategy for performance estimation is not necessarily the best one for ranking generation.