A comparative study of machine learning methods for ordinal classification with absolute and relative information
- Author
- Mengzi Tang, Raúl Pérez-Fernández and Bernard De Baets (UGent)
- Organization
- Project
- Abstract
- The performance of an ordinal classifier is highly affected by the amount of absolute information (labelled data) available for training. In order to make up for a lack of sufficient absolute information, an effective way out is to consider additional types of information. In this work, we focus on ordinal classification problems that are provided with additional relative information. We augment several classical machine learning methods by considering both absolute and relative information as constraints in the corresponding optimization problems. We compare these augmented methods on popular benchmark datasets. The experimental results show the effectivenesses of these methods for combining absolute and relative information. (C) 2021 Elsevier B.V. All rights reserved.
- Keywords
- Machine learning, Absolute information, Relative information, Ordinal classification, REGRESSION, MODELS
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Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8717769
- MLA
- Tang, Mengzi, et al. “A Comparative Study of Machine Learning Methods for Ordinal Classification with Absolute and Relative Information.” KNOWLEDGE-BASED SYSTEMS, vol. 230, 2021, doi:10.1016/j.knosys.2021.107358.
- APA
- Tang, M., Pérez-Fernández, R., & De Baets, B. (2021). A comparative study of machine learning methods for ordinal classification with absolute and relative information. KNOWLEDGE-BASED SYSTEMS, 230. https://doi.org/10.1016/j.knosys.2021.107358
- Chicago author-date
- Tang, Mengzi, Raúl Pérez-Fernández, and Bernard De Baets. 2021. “A Comparative Study of Machine Learning Methods for Ordinal Classification with Absolute and Relative Information.” KNOWLEDGE-BASED SYSTEMS 230. https://doi.org/10.1016/j.knosys.2021.107358.
- Chicago author-date (all authors)
- Tang, Mengzi, Raúl Pérez-Fernández, and Bernard De Baets. 2021. “A Comparative Study of Machine Learning Methods for Ordinal Classification with Absolute and Relative Information.” KNOWLEDGE-BASED SYSTEMS 230. doi:10.1016/j.knosys.2021.107358.
- Vancouver
- 1.Tang M, Pérez-Fernández R, De Baets B. A comparative study of machine learning methods for ordinal classification with absolute and relative information. KNOWLEDGE-BASED SYSTEMS. 2021;230.
- IEEE
- [1]M. Tang, R. Pérez-Fernández, and B. De Baets, “A comparative study of machine learning methods for ordinal classification with absolute and relative information,” KNOWLEDGE-BASED SYSTEMS, vol. 230, 2021.
@article{8717769,
abstract = {{The performance of an ordinal classifier is highly affected by the amount of absolute information (labelled data) available for training. In order to make up for a lack of sufficient absolute information, an effective way out is to consider additional types of information. In this work, we focus on ordinal classification problems that are provided with additional relative information. We augment several classical machine learning methods by considering both absolute and relative information as constraints in the corresponding optimization problems. We compare these augmented methods on popular benchmark datasets. The experimental results show the effectivenesses of these methods for combining absolute and relative information. (C) 2021 Elsevier B.V. All rights reserved.}},
articleno = {{107358}},
author = {{Tang, Mengzi and Pérez-Fernández, Raúl and De Baets, Bernard}},
issn = {{0950-7051}},
journal = {{KNOWLEDGE-BASED SYSTEMS}},
keywords = {{Machine learning,Absolute information,Relative information,Ordinal classification,REGRESSION,MODELS}},
language = {{eng}},
pages = {{15}},
title = {{A comparative study of machine learning methods for ordinal classification with absolute and relative information}},
url = {{http://doi.org/10.1016/j.knosys.2021.107358}},
volume = {{230}},
year = {{2021}},
}
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