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Authors: Tuomo Alasalmi ; Heli Koskimäki ; Jaakko Suutala and Juha Röning

Affiliation: University of Oulu, Finland

Keyword(s): Classification, Calibration.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Uncertainty in AI

Abstract: Often it is necessary to have an accurate estimate of the probability that a classifier prediction is indeed correct. Many classifiers output a prediction score that can be used as an estimate of that probability but for many classifiers these prediction scores are not well calibrated. If enough training data is available, it is possible to post process these scores by learning a mapping from the prediction scores to probabilities. One of the most used calibration algorithms is isotonic regression. This kind of calibration, however, requires a decent amount of training data to not overfit. But many real world data sets do not have excess amount of data that can be set aside for calibration. In this work, we have developed a data generation algorithm to produce more data from a limited sized training data set. We used two variations of this algorithm to generate the calibration data set for isotonic regression calibration and compared the results to the traditional approach of setting aside part of the training data for calibration. Our experimental results suggest that this can be a viable option for smaller data sets if good calibration is essential. (More)

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Paper citation in several formats:
Alasalmi, T.; Koskimäki, H.; Suutala, J. and Röning, J. (2018). Getting More Out of Small Data Sets - Improving the Calibration Performance of Isotonic Regression by Generating More Data. In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-275-2; ISSN 2184-433X, SciTePress, pages 379-386. DOI: 10.5220/0006576003790386

@conference{icaart18,
author={Tuomo Alasalmi. and Heli Koskimäki. and Jaakko Suutala. and Juha Röning.},
title={Getting More Out of Small Data Sets - Improving the Calibration Performance of Isotonic Regression by Generating More Data},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2018},
pages={379-386},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006576003790386},
isbn={978-989-758-275-2},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Getting More Out of Small Data Sets - Improving the Calibration Performance of Isotonic Regression by Generating More Data
SN - 978-989-758-275-2
IS - 2184-433X
AU - Alasalmi, T.
AU - Koskimäki, H.
AU - Suutala, J.
AU - Röning, J.
PY - 2018
SP - 379
EP - 386
DO - 10.5220/0006576003790386
PB - SciTePress

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