Machine Learning
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Data mining in metric space: an empirical analysis of supervised learning performance criteria
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An experimental comparison of performance measures for classification
Pattern Recognition Letters
On the effect of calibration in classifier combination
Applied Intelligence
Aggregative quantification for regression
Data Mining and Knowledge Discovery
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In this paper we revisit the problem of classifier calibration, motivated by the issue that existing calibration methods ignore the problem attributes (i.e., they are univariate). We propose a new calibration method inspired in binning-based methods in which the calibrated probabilities are obtained from k instances from a dataset. Bins are constructed by including the k-most similar instances, considering not only estimated probabilities but also the original attributes. This method has been tested wrt. two calibration measures, including a comparison with other traditional calibration methods. The results show that the new method outperforms the most commonly used calibration methods.