The Strength of Weak Learnability
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
Characterizing the applicability of classification algorithms using meta-level learning
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning and Its Applications
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
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
AST: Support for Algorithm Selection with a CBR Approach
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Discovering Task Neighbourhoods Through Landmark Learning Performances
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Improved Dataset Characterisation for Meta-learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
On Data and Algorithms: Understanding Inductive Performance
Machine Learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
IEEE Transactions on Knowledge and Data Engineering
The Data Mining Advisor: Meta-learning at the Service of Practitioners
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
Characteristic-Based Clustering for Time Series Data
Data Mining and Knowledge Discovery
Active Selection of Training Examples for Meta-Learning
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Introduction to Information Retrieval
Introduction to Information Retrieval
Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
New Insights into Learning Algorithms and Datasets
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
Layered concept-learning and dynamically variable bias management
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
On learning algorithm selection for classification
Applied Soft Computing
Active Generation of Training Examples in Meta-Regression
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Metalearning: Applications to Data Mining
Metalearning: Applications to Data Mining
A metric for unsupervised metalearning
Intelligent Data Analysis
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Classification algorithm selection is an open research problem whose solution has tremendous value for practitioners. In recent years, metalearning has emerged as a viable approach. Unfortunately, the ratio of examples to classes is small at the metalevel for any reasonable number of algorithms to choose from, and there are serious risks of overfitting due to underlying similarities among algorithms. To alleviate these problems, we propose to 1 cluster algorithms based on behavior similarity, and 2 redefine the metalearning task as mapping classification tasks to clusters of behaviorally-similar algorithms. Experiments with a wide range of classification tasks and algorithms demonstrate that the clustering-based selection model yields better results than typical selection models.