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
Machine Learning
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Ranking with Predictive Clustering Trees
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A higher-order approach to meta-learning
A higher-order approach to meta-learning
Predicting relative performance of classifiers from samples
ICML '05 Proceedings of the 22nd international conference on Machine learning
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Label ranking by learning pairwise preferences
Artificial Intelligence
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Decision tree and instance-based learning for label ranking
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Particle Swarm Model Selection
The Journal of Machine Learning Research
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Metalearning: Applications to Data Mining
Metalearning: Applications to Data Mining
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Meta-learning for evolutionary parameter optimization of classifiers
Machine Learning
Full model selection in the space of data mining operators
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Analysis of a random forests model
The Journal of Machine Learning Research
Selecting classification algorithms with active testing
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
A survey of intelligent assistants for data analysis
ACM Computing Surveys (CSUR)
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In this paper, we present a novel meta-feature generation method in the context of meta-learning, which is based on rules that compare the performance of individual base learners in a one-against-one manner. In addition to these new meta-features, we also introduce a new meta-learner called Approximate Ranking Tree Forests (ART Forests) that performs very competitively when compared with several state-of-the-art meta-learners. Our experimental results are based on a large collection of datasets and show that the proposed new techniques can improve the overall performance of meta-learning for algorithm ranking significantly. A key point in our approach is that each performance figure of any base learner for any specific dataset is generated by optimising the parameters of the base learner separately for each dataset.