Distance metric learning for augmenting the method of nearest neighbors for ordinal classification with absolute and relative information
- Author
- Mengzi Tang, Raul Perez Fernandez (UGent) and Bernard De Baets (UGent)
- Organization
- Project
- Abstract
- The performance of a classifier is often limited by the amount of labeled data (absolute information) available. In order to overcome this limitation, the incorporation of side information into the classification process has become a popular research topic in the field of machine learning. In this work, we propose a new method for ordinal classification that combines absolute information and a specific type of side information: relative information. In particular, this method exploits both types of information to learn an appropriate distance metric and subsequently incorporates the learned distance metric into the classical method of.. nearest neighbors. The experimental results show that the proposed method attains a good performance in terms of some of the most popular (ordinal) classification performance measures.
- Keywords
- Distance metric learning, Ordinal classification, kappa nearest neighbors, Absolute information, Relative information, CONSTRAINTS, STRATEGIES
Downloads
-
(...).pdf
- full text (Published version)
- |
- UGent only
- |
- |
- 1.55 MB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8685339
- MLA
- Tang, Mengzi, et al. “Distance Metric Learning for Augmenting the Method of Nearest Neighbors for Ordinal Classification with Absolute and Relative Information.” INFORMATION FUSION, vol. 65, 2021, pp. 72–83, doi:10.1016/j.inffus.2020.08.004.
- APA
- Tang, M., Perez Fernandez, R., & De Baets, B. (2021). Distance metric learning for augmenting the method of nearest neighbors for ordinal classification with absolute and relative information. INFORMATION FUSION, 65, 72–83. https://doi.org/10.1016/j.inffus.2020.08.004
- Chicago author-date
- Tang, Mengzi, Raul Perez Fernandez, and Bernard De Baets. 2021. “Distance Metric Learning for Augmenting the Method of Nearest Neighbors for Ordinal Classification with Absolute and Relative Information.” INFORMATION FUSION 65: 72–83. https://doi.org/10.1016/j.inffus.2020.08.004.
- Chicago author-date (all authors)
- Tang, Mengzi, Raul Perez Fernandez, and Bernard De Baets. 2021. “Distance Metric Learning for Augmenting the Method of Nearest Neighbors for Ordinal Classification with Absolute and Relative Information.” INFORMATION FUSION 65: 72–83. doi:10.1016/j.inffus.2020.08.004.
- Vancouver
- 1.Tang M, Perez Fernandez R, De Baets B. Distance metric learning for augmenting the method of nearest neighbors for ordinal classification with absolute and relative information. INFORMATION FUSION. 2021;65:72–83.
- IEEE
- [1]M. Tang, R. Perez Fernandez, and B. De Baets, “Distance metric learning for augmenting the method of nearest neighbors for ordinal classification with absolute and relative information,” INFORMATION FUSION, vol. 65, pp. 72–83, 2021.
@article{8685339,
abstract = {{The performance of a classifier is often limited by the amount of labeled data (absolute information) available. In order to overcome this limitation, the incorporation of side information into the classification process has become a popular research topic in the field of machine learning. In this work, we propose a new method for ordinal classification that combines absolute information and a specific type of side information: relative information. In particular, this method exploits both types of information to learn an appropriate distance metric and subsequently incorporates the learned distance metric into the classical method of.. nearest neighbors. The experimental results show that the proposed method attains a good performance in terms of some of the most popular (ordinal) classification performance measures.}},
author = {{Tang, Mengzi and Perez Fernandez, Raul and De Baets, Bernard}},
issn = {{1566-2535}},
journal = {{INFORMATION FUSION}},
keywords = {{Distance metric learning,Ordinal classification,kappa nearest neighbors,Absolute information,Relative information,CONSTRAINTS,STRATEGIES}},
language = {{eng}},
pages = {{72--83}},
title = {{Distance metric learning for augmenting the method of nearest neighbors for ordinal classification with absolute and relative information}},
url = {{http://doi.org/10.1016/j.inffus.2020.08.004}},
volume = {{65}},
year = {{2021}},
}
- Altmetric
- View in Altmetric
- Web of Science
- Times cited: