Calibrating Classifier Scores into Probabilities
作者： Martin Gebel , Claus Weihs
DOI: 10.1007/978-3-540-70981-7_17
关键词:
摘要: This paper provides an overview of calibration methods for supervised classification learners. Calibration means a scaling classifier scores into the probability space. Such probabilistic output is especially useful if used post-processing. The calibraters are compared by using 10-fold cross-validation according to their performance on SVM and CART outputs four different two-class data sets.
springer.com
sci-hub.se 参考文章(4)
Ursula Maria Garczarek, Classification rules in standardized partition spaces Universität Dortmund. ,(2002) , 10.17877/DE290R-14912
John C. Platt, Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods Advances in Large Margin Classifiers. ,(1999)
Bianca Zadrozny, Charles Elkan, Transforming classifier scores into accurate multiclass probability estimates Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '02. pp. 694- 699 ,(2002) , 10.1145/775047.775151
Paul N. Bennett, Using asymmetric distributions to improve text classifier probability estimates international acm sigir conference on research and development in information retrieval. pp. 111- 118 ,(2003) , 10.1145/860435.860457