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Machine learning mo...
Machine learning models for automatic labeling: A systematic literature review
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- Fredriksson, Teodor, 1992 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology,Department of Computer Science and Engineering, Division of Software Engineering, Chalmers University of Technology, Gothenburg, Sweden
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- Bosch, Jan, 1967 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology,Department of Computer Science and Engineering, Division of Software Engineering, Chalmers University of Technology, Gothenburg, Sweden
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- Olsson, Helena Holmström (författare)
- Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT)
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(creator_code:org_t)
- SCITEPRESS - Science and Technology Publications, 2020
- 2020
- Engelska.
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Ingår i: ICSOFT 2020 - Proceedings of the 15th International Conference on Software Technologies. - : SCITEPRESS - Science and Technology Publications. ; , s. 552-566
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Language Technology (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Automatic labeling
- Semi-supervised learning
- Active machine learning
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)