Work

Binary Classifier Calibration Using a Bayesian Non-Parametric Approach

Year: 2015

Type: conference-paper

Abstract: Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in Data mining. This paper presents two new non-parametric methods for calibrating outputs of binary classification models: a method based on the Bayes optimal selection and a method based on the Bayesian model averaging. The advantage of these methods is that they are independent of the algorithm used to learn a predictive model, and they can be applied in a post-processing step, after the model is learned. This makes them applicable to a wide variety of machine learning models and methods. These calibration methods, as well as other methods, are tested on a variety of datasets in terms of both discrimination and calibration performance. The results show the methods either outperform or are comparable in performance to the state-of-the-art calibration methods.

Source: SIAM International Conference on Data Mining

DOI: 10.1137/1.9781611974010.24

Authors: Mahdi Pakdaman Naeini, Gregory F. Cooper, Milos Hauskrecht

Institution: University of Pittsburgh

Language: English

Topic: Imbalanced Data Classification Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green