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Distance metric learning for augmenting the method of nearest neighbors for ordinal classification with absolute and relative information

Mengzi Tang (UGent) , Raul Perez Fernandez (UGent) and Bernard De Baets (UGent)
(2021) INFORMATION FUSION. 65. p.72-83
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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

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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://dx.doi.org/10.1016/j.inffus.2020.08.004}},
  volume       = {{65}},
  year         = {{2021}},
}

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