Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ordering by weighted number of wins gives a good ranking for weighted tournaments
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
SIAM Journal on Discrete Mathematics
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Noise Tolerant Variants of the Perceptron Algorithm
The Journal of Machine Learning Research
Label ranking by learning pairwise preferences
Artificial Intelligence
DS'10 Proceedings of the 13th international conference on Discovery science
Preferences in AI: An overview
Artificial Intelligence
Context-dependent feedback prioritisation in exploratory learning revisited
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
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
Mixtures of weighted distance-based models for ranking data with applications in political studies
Computational Statistics & Data Analysis
Probability estimation for multi-class classification based on label ranking
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Multilayer perceptron for label ranking
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Multi-prototype label ranking with novel pairwise-to-total-rank aggregation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Pairwise meta-rules for better meta-learning-based algorithm ranking
Machine Learning
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The label ranking problem consists of learning a model that maps instances to total orders over a finite set of predefined labels. This paper introduces new methods for label ranking that complement and improve upon existing approaches. More specifically, we propose extensions of two methods that have been used extensively for classification and regression so far, namely instance-based learning and decision tree induction. The unifying element of the two methods is a procedure for locally estimating predictive probability models for label rankings.