Similarity-Binning Averaging: A Generalisation Of Binning Calibration
IDEAL'09: Proceedings of the 10th international conference on Intelligent data engineering and automated learning(2009)
摘要
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 flew method outperforms the, most commonly used calibration methods.
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关键词
calibration method,calibration measure,classifier calibration,new calibration method,traditional calibration method,binning-based method,new method,problem attribute,k instance,k-most similar instance,Similarity-binning averaging,binning calibration
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