Anti-noise twin-hyperspheres with density fuzzy for binary classification to imbalanced data with noise
Abstract This paper presents twin-hyperspheres of resisting noise for binary classification to imbalanced data with noise. First, employing the decision of evaluating the contributions created by points for the training of the hyperspheres, then the label density estimator is introduced into the fuz...
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| Format: | Article |
| Language: | English |
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Springer
2023-07-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-023-01089-1 |
| _version_ | 1797647124077215744 |
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| author | Jian Zheng |
| author_facet | Jian Zheng |
| author_sort | Jian Zheng |
| collection | DOAJ |
| description | Abstract This paper presents twin-hyperspheres of resisting noise for binary classification to imbalanced data with noise. First, employing the decision of evaluating the contributions created by points for the training of the hyperspheres, then the label density estimator is introduced into the fuzzy membership to quantize the provided contributions, and finally, unknown points can be assigned into corresponding classes. Utilizing the decision, the interference created by the noise hidden in the data is suppressed. Experiment results show that when noise ratio reaches 90%, classification accuracies of the model are 0.802, 0.611 on the synthetic datasets and UCI datasets containing Gaussian noise, respectively. Classification results of the model outperform these of the competitors, and these boundaries learned by the model to separate noise from majority classes and minority classes are superior to these learned by the competitors. Moreover, efforts gained by the proposed density fuzzy are effectiveness in noise resistance; meanwhile, the density fuzzy does not rely on specific classifiers or specific scenarios. |
| first_indexed | 2024-03-11T15:11:49Z |
| format | Article |
| id | doaj.art-72a23dd17784489fbd8769a74a9f4a7c |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-03-11T15:11:49Z |
| publishDate | 2023-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-72a23dd17784489fbd8769a74a9f4a7c2023-10-29T12:41:33ZengSpringerComplex & Intelligent Systems2199-45362198-60532023-07-01966103611610.1007/s40747-023-01089-1Anti-noise twin-hyperspheres with density fuzzy for binary classification to imbalanced data with noiseJian Zheng0College of Artificial Intelligence, Chongqing Technology and Business UniversityAbstract This paper presents twin-hyperspheres of resisting noise for binary classification to imbalanced data with noise. First, employing the decision of evaluating the contributions created by points for the training of the hyperspheres, then the label density estimator is introduced into the fuzzy membership to quantize the provided contributions, and finally, unknown points can be assigned into corresponding classes. Utilizing the decision, the interference created by the noise hidden in the data is suppressed. Experiment results show that when noise ratio reaches 90%, classification accuracies of the model are 0.802, 0.611 on the synthetic datasets and UCI datasets containing Gaussian noise, respectively. Classification results of the model outperform these of the competitors, and these boundaries learned by the model to separate noise from majority classes and minority classes are superior to these learned by the competitors. Moreover, efforts gained by the proposed density fuzzy are effectiveness in noise resistance; meanwhile, the density fuzzy does not rely on specific classifiers or specific scenarios.https://doi.org/10.1007/s40747-023-01089-1Binary classificationDensity fuzzyHyperspheresImbalanced dataNoise |
| spellingShingle | Jian Zheng Anti-noise twin-hyperspheres with density fuzzy for binary classification to imbalanced data with noise Complex & Intelligent Systems Binary classification Density fuzzy Hyperspheres Imbalanced data Noise |
| title | Anti-noise twin-hyperspheres with density fuzzy for binary classification to imbalanced data with noise |
| title_full | Anti-noise twin-hyperspheres with density fuzzy for binary classification to imbalanced data with noise |
| title_fullStr | Anti-noise twin-hyperspheres with density fuzzy for binary classification to imbalanced data with noise |
| title_full_unstemmed | Anti-noise twin-hyperspheres with density fuzzy for binary classification to imbalanced data with noise |
| title_short | Anti-noise twin-hyperspheres with density fuzzy for binary classification to imbalanced data with noise |
| title_sort | anti noise twin hyperspheres with density fuzzy for binary classification to imbalanced data with noise |
| topic | Binary classification Density fuzzy Hyperspheres Imbalanced data Noise |
| url | https://doi.org/10.1007/s40747-023-01089-1 |
| work_keys_str_mv | AT jianzheng antinoisetwinhypersphereswithdensityfuzzyforbinaryclassificationtoimbalanceddatawithnoise |