On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Some Varieties of Qualitative Probability
IPMU'94 Selected papers from the 5th International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems, Advances in Intelligent Computing
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
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
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
ECML '07 Proceedings of the 18th European conference on Machine Learning
Decision tree and instance-based learning for label ranking
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
The Journal of Machine Learning Research
| Hi-index | 0.00 |
We consider the problem of probability estimation in the setting of multi-class classification. While this problem has already been addressed in the literature, we tackle it from a novel perspective. Exploiting the close connection between probability estimation and ranking, our idea is to solve the former on the basis of the latter, taking advantage of recently developed methods for label ranking. More specifically, we argue that the Plackett-Luce ranking model is a very natural choice in this context, especially as it can be seen as a multinomial extension of the Bradley-Terry model. The latter provides the basis of pairwise coupling techniques, which arguably constitute the state-of-the-art in multi-class probability estimation. We explore the relationship between the pairwise and the ranking-based approach to probability estimation, both formally and empirically. Using synthetic and real-world data, we show that our method does not only enjoy nice theoretical properties, but is also competitive in terms of accuracy and efficiency.