# Imbalanced Data Classification Techniques

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/imbalanced-data-classification-techniques-2/

## Facts

| Field | Value |
| --- | --- |
| Citations | 600,594 |
| Description | This cluster of papers focuses on the challenges and techniques for handling imbalanced data in classification problems. It covers methods such as SMOTE, ROC analysis, cost-sensitive learning, ensemble methods, and their applications in fraud detection. The cluster also discusses the use of precision-recall and boosting algorithms, as well as the effectiveness of random forest in addressing imbalanced datasets. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | https://openalex.org/T11652 |
| Works | 39,625 |

## Topic researchers

Showing 12 of 20.

- [Yoshua Bengio](https://scholariq.org/researchers/yoshua-bengio/)
- [Robert Tibshirani](https://scholariq.org/researchers/robert-tibshirani/)
- [Gregory Y.H. Lip](https://scholariq.org/researchers/gregory-y-h-lip/)
- [Trevor Hastie](https://scholariq.org/researchers/trevor-hastie/)
- [Daniel Levy](https://scholariq.org/researchers/daniel-levy/)
- [Leo Breiman](https://scholariq.org/researchers/leo-breiman/)
- [Jerome H. Friedman](https://scholariq.org/researchers/jerome-h-friedman/)
- [John N. Weinstein](https://scholariq.org/researchers/john-n-weinstein/)
- [David Haussler](https://scholariq.org/researchers/david-haussler/)
- [Ewout W. Steyerberg](https://scholariq.org/researchers/ewout-w-steyerberg/)
- [Bernhard Schölkopf](https://scholariq.org/researchers/bernhard-scholkopf/)
- [Francisco Herrera](https://scholariq.org/researchers/francisco-herrera/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.