A Hybrid Approach for Binary Classification of Imbalanced Data Article Swipe
Hsinhan Tsai
,
Ta-Wei Yang
,
Wai-Man Wong
,
Cheng‐Fu Chou
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2207.02738
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2207.02738
Binary classification with an imbalanced dataset is challenging. Models tend to consider all samples as belonging to the majority class. Although existing solutions such as sampling methods, cost-sensitive methods, and ensemble learning methods improve the poor accuracy of the minority class, these methods are limited by overfitting problems or cost parameters that are difficult to decide. We propose HADR, a hybrid approach with dimension reduction that consists of data block construction, dimentionality reduction, and ensemble learning with deep neural network classifiers. We evaluate the performance on eight imbalanced public datasets in terms of recall, G-mean, and AUC. The results show that our model outperforms state-of-the-art methods.
Related Topics
Concepts
Overfitting
Artificial intelligence
Machine learning
Computer science
Class (philosophy)
Binary classification
Dimensionality reduction
Ensemble learning
Reduction (mathematics)
Binary number
Block (permutation group theory)
Artificial neural network
Dimension (graph theory)
Pattern recognition (psychology)
Data mining
Mathematics
Support vector machine
Geometry
Arithmetic
Pure mathematics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2207.02738
- https://arxiv.org/pdf/2207.02738
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4284960596
All OpenAlex metadata
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https://openalex.org/W4284960596Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2207.02738Digital Object Identifier
- Title
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A Hybrid Approach for Binary Classification of Imbalanced DataWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-07-06Full publication date if available
- Authors
-
Hsinhan Tsai, Ta-Wei Yang, Wai-Man Wong, Cheng‐Fu ChouList of authors in order
- Landing page
-
https://arxiv.org/abs/2207.02738Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2207.02738Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2207.02738Direct OA link when available
- Concepts
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Overfitting, Artificial intelligence, Machine learning, Computer science, Class (philosophy), Binary classification, Dimensionality reduction, Ensemble learning, Reduction (mathematics), Binary number, Block (permutation group theory), Artificial neural network, Dimension (graph theory), Pattern recognition (psychology), Data mining, Mathematics, Support vector machine, Geometry, Arithmetic, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.evaluate | 82 |
| abstract_inverted_index.existing | 21 |
| abstract_inverted_index.learning | 31, 75 |
| abstract_inverted_index.majority | 18 |
| abstract_inverted_index.methods, | 26, 28 |
| abstract_inverted_index.methods. | 105 |
| abstract_inverted_index.minority | 39 |
| abstract_inverted_index.problems | 47 |
| abstract_inverted_index.sampling | 25 |
| abstract_inverted_index.belonging | 15 |
| abstract_inverted_index.difficult | 53 |
| abstract_inverted_index.dimension | 63 |
| abstract_inverted_index.reduction | 64 |
| abstract_inverted_index.solutions | 22 |
| abstract_inverted_index.imbalanced | 4, 87 |
| abstract_inverted_index.parameters | 50 |
| abstract_inverted_index.reduction, | 72 |
| abstract_inverted_index.outperforms | 103 |
| abstract_inverted_index.overfitting | 46 |
| abstract_inverted_index.performance | 84 |
| abstract_inverted_index.challenging. | 7 |
| abstract_inverted_index.classifiers. | 80 |
| abstract_inverted_index.construction, | 70 |
| abstract_inverted_index.classification | 1 |
| abstract_inverted_index.cost-sensitive | 27 |
| abstract_inverted_index.dimentionality | 71 |
| abstract_inverted_index.state-of-the-art | 104 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/1 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | No poverty |
| citation_normalized_percentile |