NI-MWMOTE: An improving noise-immunity majority weighted minority oversampling technique for imbalanced classification problems
• New noise-immunity oversampling method for imbalanced classification problems. • It adaptively processes noise based on probability and misclassification error. • It simplifies the minority class clustering process using unsupervised clustering. • It adaptively determines the sub-cluster size bein...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wei, Jianan [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Do denture processing techniques affect the mechanical properties of denture teeth? - Clements, Jody L. ELSEVIER, 2017, an international journal, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:158 ; year:2020 ; day:15 ; month:11 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.eswa.2020.113504 |
|---|
| Katalog-ID: |
ELV051329026 |
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| 520 | |a • New noise-immunity oversampling method for imbalanced classification problems. • It adaptively processes noise based on probability and misclassification error. • It simplifies the minority class clustering process using unsupervised clustering. • It adaptively determines the sub-cluster size being sampled using misclassification error. • Generated synthetic instances using MWMOTE improved subsequent classification. | ||
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| 650 | 7 | |a Oversampling |2 Elsevier | |
| 650 | 7 | |a Clustering |2 Elsevier | |
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| 700 | 1 | |a Hu, Yao |4 oth | |
| 700 | 1 | |a Fan, Qingsong |4 oth | |
| 700 | 1 | |a Huang, Dong |4 oth | |
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• New noise-immunity oversampling method for imbalanced classification problems. • It adaptively processes noise based on probability and misclassification error. • It simplifies the minority class clustering process using unsupervised clustering. • It adaptively determines the sub-cluster size being sampled using misclassification error. • Generated synthetic instances using MWMOTE improved subsequent classification. |
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• New noise-immunity oversampling method for imbalanced classification problems. • It adaptively processes noise based on probability and misclassification error. • It simplifies the minority class clustering process using unsupervised clustering. • It adaptively determines the sub-cluster size being sampled using misclassification error. • Generated synthetic instances using MWMOTE improved subsequent classification. |
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• New noise-immunity oversampling method for imbalanced classification problems. • It adaptively processes noise based on probability and misclassification error. • It simplifies the minority class clustering process using unsupervised clustering. • It adaptively determines the sub-cluster size being sampled using misclassification error. • Generated synthetic instances using MWMOTE improved subsequent classification. |
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