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Calibration Techniques for Binary Classification Problems: A Comparative Analysis

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Learning Paradigms and Algorithms; Stochastic Learning and Statistical Algorithms ; Support Vector Machines and Kernel Methods

Authors: Alessio Martino ; Enrico De Santis ; Luca Baldini and Antonello Rizzi

Affiliation: Department of Information Engineering, Electronics and Telecommunications, University of Rome ”La Sapienza”, Via Eudossiana 18, 00184 Rome and Italy

ISBN: 978-989-758-384-1

Keyword(s): Calibration, Classification, Supervised Learning, Support Vector Machine, Probability Estimates.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

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.

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Paper citation in several formats:
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 - Volume 1: NCTA, (IJCCI 2019) ISBN 978-989-758-384-1, pages 487-495. DOI: 10.5220/0008165504870495

@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 - Volume 1: NCTA, (IJCCI 2019)},
year={2019},
pages={487-495},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008165504870495},
isbn={978-989-758-384-1},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2019)
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

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