Machine learning, neural and statistical classification
Estimating the Predictive Accuracy of a Classifier
EMCL '01 Proceedings of the 12th 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
Experiments in Meta-level Learning with ILP
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
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
Zoomed Ranking: Selection of Classification Algorithms Based on Relevant Performance Information
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
On Data and Algorithms: Understanding Inductive Performance
Machine Learning
Kernelizing the output of tree-based methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
DS'10 Proceedings of the 13th international conference on Discovery science
Empirical evaluation of ranking prediction methods for gene expression data classification
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Mining association rules for label ranking
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Hierarchical multi-classification with predictive clustering trees in functional genomics
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
Learning predictive clustering rules
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
A survey of intelligent assistants for data analysis
ACM Computing Surveys (CSUR)
Pairwise meta-rules for better meta-learning-based algorithm ranking
Machine Learning
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A novel class of applications of predictive clustering trees is addressed, namely ranking. Predictive clustering trees, as implemented in CLUS, allow for predicting multiple target variables. This approach makes sense especially if the target variables are not independent of each other. This is typically the case in ranking, where the (relative) performance of several approaches on the same task has to be predicted from a given description of the task. We propose to use predictive clustering trees for ranking. As compared to existing ranking approaches which are instance-based, our approach also allows for an explanation of the predicted rankings. We illustrate our approach on the task of ranking machine learning algorithms, where the (relative) performance of the learning algorithms on a dataset has to be predicted from a given dataset description.