COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Combining Classifiers with Meta Decision Trees
Machine Learning
Machine Learning
Machine Learning
Estimating the Predictive Accuracy of a Classifier
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ActiveCP: A Method for Speeding up User Preferences Acquisition in Collaborative Filtering Systems
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Active Hidden Markov Models for Information Extraction
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
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
Diverse ensembles for active learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Predicting relative performance of classifiers from samples
ICML '05 Proceedings of the 22nd international conference on Machine learning
Active Learning with Feedback on Features and Instances
The Journal of Machine Learning Research
Selective generation of training examples in active meta-learning
International Journal of Hybrid Intelligent Systems - HIS 2007
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Metalearning: Applications to Data Mining
Metalearning: Applications to Data Mining
UCI++: Improved Support for Algorithm Selection Using Datasetoids
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
An analysis of active learning strategies for sequence labeling tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Active learning with multiple views
Journal of Artificial Intelligence Research
Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy
IEEE Transactions on Fuzzy Systems
Combining Uncertainty Sampling Methods for Active Meta-Learning
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Active Generation of Training Examples in Meta-Regression
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Active learning with committees for text categorization
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Maximum Ambiguity-Based Sample Selection in Fuzzy Decision Tree Induction
IEEE Transactions on Knowledge and Data Engineering
Information Sciences: an International Journal
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Meta-Learning aims to automatically acquire knowledge relating features of learning problems to the performance of learning algorithms. Each training example in Meta-Learning (i.e. each meta-example) stores features of a learning problem plus the performance obtained by a set of algorithms when evaluated on the problem. Based on a set of meta-examples, a Meta-Learner will be used to predict algorithm performance for new problems. The generation of a good set of meta-examples can be a costly process, since for each problem it is necessary to perform an empirical evaluation of the algorithms. In a previous work, we proposed the Active Meta-Learning, in which Active Learning was used to reduce the set of meta-examples by selecting only the most relevant problems for meta-example generation. In the current work, we extend our previous research by combining different Uncertainty Sampling methods for Active Meta-Learning, considering that each individual method will provide useful information to select relevant problems. We also investigated the use of Outlier Detection to remove a priori those problems considered as outliers, aiming to improve the performance of the sampling methods. In our experiments, we observed a gain in Meta-Learning performance when the proposed combining method was compared to the individual active methods being combined and also when outliers were removed from the set of problems available for meta-example generation.