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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Oghenejokpeme I., Orhobor | - |
| dc.contributor.author | Nastasiya F., Grinberg | - |
| dc.contributor.author | Larisa N., Soldatova | - |
| dc.date.accessioned | 2023-03-30T07:04:50Z | - |
| dc.date.available | 2023-03-30T07:04:50Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | https://link.springer.com/article/10.1007/s10994-022-06199-4 | - |
| dc.identifier.uri | https://dlib.phenikaa-uni.edu.vn/handle/PNK/7337 | - |
| dc.description | CC BY | vi |
| dc.description.abstract | We present an extension to the federated ensemble regression using classification algorithm, an ensemble learning algorithm for regression problems which leverages the distribution of the samples in a learning set to achieve improved performance. We evaluated the extension using four classifiers and four regressors, two discretizers, and 119 responses from a wide variety of datasets in different domains. | vi |
| dc.language.iso | en | vi |
| dc.publisher | Springer | vi |
| dc.subject | classification algorithm | vi |
| dc.subject | different domains | vi |
| dc.title | Imbalanced regression using regressor-classifier ensembles | vi |
| dc.type | Book | vi |
| Appears in Collections | OER - Công nghệ thông tin | |
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