On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Improved Pairwise Coupling Classification with Correcting Classifiers
ECML '98 Proceedings of the 10th European Conference on Machine Learning
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Transforming classifier scores into accurate multiclass probability estimates
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
Probabilistic score estimation with piecewise logistic regression
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Predicting good probabilities with supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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Pattern Recognition Letters
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Intelligent Data Analysis
Learning valued preference structures for solving classification problems
Fuzzy Sets and Systems
Label ranking by learning pairwise preferences
Artificial Intelligence
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
On predictive accuracy and risk minimization in pairwise label ranking
Journal of Computer and System Sciences
Polychotomous classification with pairwise classifiers: a new voting principle
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
A transitivity analysis of bipartite rankings in pairwise multi-class classification
Information Sciences: an International Journal
A probabilistic model of classifier competence for dynamic ensemble selection
Pattern Recognition
Learning from label preferences
DS'11 Proceedings of the 14th international conference on Discovery science
A class of fuzzy multisets with a fixed number of memberships
Information Sciences: an International Journal
An alternative to fuzzy methods in decision-making problems
Expert Systems with Applications: An International Journal
Signatures: Definitions, operators and applications to fuzzy modelling
Fuzzy Sets and Systems
Efficient pairwise classification using local cross off strategy
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
A survey of multiple classifier systems as hybrid systems
Information Fusion
Information Sciences: an International Journal
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Weighted voting is the commonly used strategy for combining predictions in pairwise classification. Even though it shows good classification performance in practice, it is often criticized for lacking a sound theoretical justification. In this paper, we study the problem of combining predictions within a formal framework of label ranking and, under some model assumptions, derive a generalized voting strategy in which predictions are properly adapted according to the strengths of the corresponding base classifiers. We call this strategy adaptive voting and show that it is optimal in the sense of yielding a MAP prediction of the class label of a test instance. Moreover, we offer a theoretical justification for weighted voting by showing that it yields a good approximation of the optimal adaptive voting prediction. This result is further corroborated by empirical evidence from experiments with real and synthetic data sets showing that, even though adaptive voting is sometimes able to achieve consistent improvements, weighted voting is in general quite competitive, all the more in cases where the aforementioned model assumptions underlying adaptive voting are not met. In this sense, weighted voting appears to be a more robust aggregation strategy.