Balancing large margin nearest neighbours for imbalanced data Article Swipe
Xiaotian Zhang
,
Nan Han
,
Shaojie Qiao
,
Yongqing Zhang
,
Ping Huang
,
Jing Peng
,
Kai Zhou
,
Changan Yuan
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1049/joe.2019.1178
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1049/joe.2019.1178
It is critical to learn and obtain a good distance metric that can precisely measure the distance between samples in imbalanced data. However, traditional metric learning algorithms, e.g. large margin nearest neighbour (LMNN), information‐theoretic metric learning, neighbourhood component analysis, do not take imbalanced distributions of classes into consideration. The traditional methods are apt to be affected by the majority samples, so those important minority samples are often ignored during the learning phase of distance metrics matrix, this may gravely confuse decision‐making systems on classifying samples. In order to resolve this problem, the authors propose a novel metric‐learning method named balancing large margin nearest neighbour (BLMNN) for imbalanced data. BLMNN can improve the objective function according to the distribution of classes, which treats the minority and majority classes equally during the optimisation process. Thus, the contribution of minority class is taken into full consideration, which can greatly improve the accuracy of classification. Substantial experiments were performed on real‐world imbalanced datasets. The experiments results in various evaluation indexes of the proposed method comparing it with other metric‐learning methods show the advantages of the proposed method.
Related Topics
Concepts
Metric (unit)
Margin (machine learning)
Large margin nearest neighbor
Computer science
k-nearest neighbors algorithm
Artificial intelligence
Machine learning
Pattern recognition (psychology)
Class (philosophy)
Neighbourhood (mathematics)
Data mining
Mathematics
Economics
Mathematical analysis
Operations management
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/joe.2019.1178
- https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/joe.2019.1178
- OA Status
- gold
- Cited By
- 5
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3046166968
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3046166968Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1049/joe.2019.1178Digital Object Identifier
- Title
-
Balancing large margin nearest neighbours for imbalanced dataWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-07-01Full publication date if available
- Authors
-
Xiaotian Zhang, Nan Han, Shaojie Qiao, Yongqing Zhang, Ping Huang, Jing Peng, Kai Zhou, Changan YuanList of authors in order
- Landing page
-
https://doi.org/10.1049/joe.2019.1178Publisher landing page
- PDF URL
-
https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/joe.2019.1178Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/joe.2019.1178Direct OA link when available
- Concepts
-
Metric (unit), Margin (machine learning), Large margin nearest neighbor, Computer science, k-nearest neighbors algorithm, Artificial intelligence, Machine learning, Pattern recognition (psychology), Class (philosophy), Neighbourhood (mathematics), Data mining, Mathematics, Economics, Mathematical analysis, Operations managementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1, 2022: 1, 2021: 1, 2020: 2Per-year citation counts (last 5 years)
- References (count)
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15Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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