Observation points classifier ensemble for high‐dimensional imbalanced classification Article Swipe
Yulin He
,
Xu Li
,
Philippe Fournier‐Viger
,
Joshua Zhexue Huang
,
Mianjie Li
,
Salman Salloum
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1049/cit2.12100
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1049/cit2.12100
In this paper, an Observation Points Classifier Ensemble (OPCE) algorithm is proposed to deal with High‐Dimensional Imbalanced Classification (HDIC) problems based on data processed using the Multi‐Dimensional Scaling (MDS) feature extraction technique. First, dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as possible. Second, a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low‐dimensional data space. Third, optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown samples. Exhaustive experiments have been conducted to evaluate the feasibility, rationality, and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data sets. Experimental results show that (1) the OPCE algorithm can be trained faster on low‐dimensional imbalanced data than on high‐dimensional data; (2) the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased; and (3) statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC algorithms. This demonstrates that OPCE is a viable algorithm to deal with HDIC problems.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/cit2.12100
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/cit2.12100
- OA Status
- gold
- Cited By
- 5
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4225410898
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4225410898Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1049/cit2.12100Digital Object Identifier
- Title
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Observation points classifier ensemble for high‐dimensional imbalanced classificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-05-04Full publication date if available
- Authors
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Yulin He, Xu Li, Philippe Fournier‐Viger, Joshua Zhexue Huang, Mianjie Li, Salman SalloumList of authors in order
- Landing page
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https://doi.org/10.1049/cit2.12100Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/cit2.12100Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/cit2.12100Direct OA link when available
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Classifier (UML), Curse of dimensionality, Pattern recognition (psychology), Computer science, Artificial intelligence, Algorithm, Mathematics, Data miningTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2024: 4, 2023: 1Per-year citation counts (last 5 years)
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32Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2022-05-04 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2154706222, https://openalex.org/W2962981269, https://openalex.org/W1754612754, https://openalex.org/W1517113043, https://openalex.org/W2172611079, https://openalex.org/W2408246687, https://openalex.org/W2148143831, https://openalex.org/W2766296277, https://openalex.org/W1965337628, https://openalex.org/W2908285635, https://openalex.org/W3087306757, https://openalex.org/W2131391419, https://openalex.org/W2015283109, https://openalex.org/W1975664724, https://openalex.org/W2792730533, https://openalex.org/W2540352327, https://openalex.org/W3185543926, https://openalex.org/W2156571267, https://openalex.org/W3093372050, https://openalex.org/W2152195021, https://openalex.org/W1912982817, https://openalex.org/W2135493362, https://openalex.org/W61851215, https://openalex.org/W1565746575, https://openalex.org/W2773612851, https://openalex.org/W2317515691, https://openalex.org/W2560070550, https://openalex.org/W2593914038, https://openalex.org/W2910967743, https://openalex.org/W3200959564, https://openalex.org/W2010334716, https://openalex.org/W3036600133 |
| referenced_works_count | 32 |
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| abstract_inverted_index.assessment | 93 |
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| corresponding_author_ids | https://openalex.org/A5076542236 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I180726961 |
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| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |