Calibration Techniques for Binary Classification Problems: A Comparative Analysis

Alessio Martino, Enrico De Santis, Luca Baldini, Antonello Rizzi

2019

Abstract

Calibrating a classification system consists in transforming the output scores, which somehow state the confidence of the classifier regarding the predicted output, into proper probability estimates. Having a well-calibrated classifier has a non-negligible impact on many real-world applications, for example decision making systems synthesis for anomaly detection/fault prediction. In such industrial scenarios, risk assessment is certainly related to costs which must be covered. In this paper we review three state-of-the-art calibration techniques (Platt’s Scaling, Isotonic Regression and SplineCalib) and we propose three lightweight procedures based on a plain fitting of the reliability diagram. Computational results show that the three proposed techniques have comparable performances with respect to the three state-of-the-art approaches.

Paper Citation

in Harvard Style

Martino A., De Santis E., Baldini L. and Rizzi A. (2019). Calibration Techniques for Binary Classification Problems: A Comparative Analysis. In Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: NCTA; ISBN 978-989-758-384-1, SciTePress, pages 487-495. DOI: 10.5220/0008165504870495

in Bibtex Style

@conference{ncta19,
author={Alessio Martino and Enrico De Santis and Luca Baldini and Antonello Rizzi},
title={Calibration Techniques for Binary Classification Problems: A Comparative Analysis},
booktitle={Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: NCTA},
year={2019},
pages={487-495},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008165504870495},
isbn={978-989-758-384-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: NCTA
TI - Calibration Techniques for Binary Classification Problems: A Comparative Analysis
SN - 978-989-758-384-1
AU - Martino A.
AU - De Santis E.
AU - Baldini L.
AU - Rizzi A.
PY - 2019
SP - 487
EP - 495
DO - 10.5220/0008165504870495
PB - SciTePress