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Authors: Teodor Fredriksson 1 ; Jan Bosch 1 and Helena Holmstrm Olsson 2

Affiliations: 1 Department of Computer Science and Engineering, Division of Software Engineering, Chalmers University of Technology, Gothenburg, Sweden ; 2 Department of Computer Science and Media Technology, Malm University, Malm, Sweden

Keyword(s): Semi-supervised Learning, Active Machine Learning, Automatic Labeling.

Abstract: Automatic labeling is a type of classification problem. Classification has been studied with the help of statistical methods for a long time. With the explosion of new better computer processing units (CPUs) and graphical processing units (GPUs) the interest in machine learning has grown exponentially and we can use both statistical learning algorithms as well as deep neural networks (DNNs) to solve the classification tasks. Classification is a supervised machine learning problem and there exists a large amount of methodology for performing such task. However, it is very rare in industrial applications that data is fully labeled which is why we need good methodology to obtain error-free labels. The purpose of this paper is to examine the current literature on how to perform labeling using ML, we will compare these models in terms of popularity and on what datatypes they are used on. We performed a systematic literature review of empirical studies for machine learning for labeling. We identified 43 primary studies relevant to our search. From this we were able to determine the most common machine learning models for labeling. Lack of unlabeled instances is a major problem for industry as supervised learning is the most widely used. Obtaining labels is costly in terms of labor and financial costs. Based on our findings in this review we present alternate ways for labeling data for use in supervised learning tasks. (More)

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Paper citation in several formats:
Fredriksson, T.; Bosch, J. and Olsson, H. (2020). Machine Learning Models for Automatic Labeling: A Systematic Literature Review. In Proceedings of the 15th International Conference on Software Technologies - ICSOFT; ISBN 978-989-758-443-5; ISSN 2184-2833, SciTePress, pages 552-561. DOI: 10.5220/0009972705520561

@conference{icsoft20,
author={Teodor Fredriksson. and Jan Bosch. and Helena Holmstrm Olsson.},
title={Machine Learning Models for Automatic Labeling: A Systematic Literature Review},
booktitle={Proceedings of the 15th International Conference on Software Technologies - ICSOFT},
year={2020},
pages={552-561},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009972705520561},
isbn={978-989-758-443-5},
issn={2184-2833},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Software Technologies - ICSOFT
TI - Machine Learning Models for Automatic Labeling: A Systematic Literature Review
SN - 978-989-758-443-5
IS - 2184-2833
AU - Fredriksson, T.
AU - Bosch, J.
AU - Olsson, H.
PY - 2020
SP - 552
EP - 561
DO - 10.5220/0009972705520561
PB - SciTePress

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