Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Average case analysis of k-CNF and k-DNF learning algorithms
Proceedings of the workshop on Computational learning theory and natural learning systems (vol. 2) : intersections between theory and experiment: intersections between theory and experiment
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Recursive automatic algorithm selection for inductive learning
Recursive automatic algorithm selection for inductive learning
Error reduction through learning multiple descriptions
Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
Ranking with Predictive Clustering Trees
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Characterization of Classification Algorithms
EPIA '95 Proceedings of the 7th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Generalizing from Case studies: A Case Study
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
Characterizing Model Erros and Differences
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Experiments in Meta-level Learning with ILP
PKDD '99 Proceedings of the Third 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
Tractable Average-Case Analysis of Naive Bayesian Classifiers
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Introduction to the Special Issue on Meta-Learning
Machine Learning
Minority report in fraud detection: classification of skewed data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Machine learning: a review of classification and combining techniques
Artificial Intelligence Review
Selective generation of training examples in active meta-learning
International Journal of Hybrid Intelligent Systems - HIS 2007
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
Supervised Machine Learning: A Review of Classification Techniques
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
Active Generation of Training Examples in Meta-Regression
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
An Analysis of Meta-learning Techniques for Ranking Clustering Algorithms Applied to Artificial Data
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 to support the generation of meta-examples
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Combining Uncertainty Sampling methods for supporting the generation of meta-examples
Information Sciences: an International Journal
Information Technology and Management
Computational Statistics & Data Analysis
A feature subset selection algorithm automatic recommendation method
Journal of Artificial Intelligence Research
A metric for unsupervised metalearning
Intelligent Data Analysis
Automatic selection of classification learning algorithms for data mining practitioners
Intelligent Data Analysis
A novel feature subset selection algorithm based on association rule mining
Intelligent Data Analysis
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In this paper we address two symmetrical issues, the discovery of similarities among classification algorithms, and among datasets. Both on the basis of error measures, which we use to define the error correlation between two algorithms, and determine the relative performance of a list of algorithms. We use the first to discover similarities between learners, and both of them to discover similarities between datasets. The latter sketch maps on the dataset space. Regions within each map exhibit specific patterns of error correlation or relative performance. To acquire an understanding of the factors determining these regions we describe them using simple characteristics of the datasets. Descriptions of each region are given in terms of the distributions of dataset characteristics within it.