A Review of Machine Learning Techniques in Imbalanced Data and Future Trends
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909772920389632 |
|---|---|
| author | Jafarigol, Elaheh Trafalis, Theodore Mohammadi, Neshat |
| author_facet | Jafarigol, Elaheh Trafalis, Theodore Mohammadi, Neshat |
| contents | For over two decades, detecting rare events has been a challenging task among researchers in the data mining and machine learning domain. Real-life problems inspire researchers to navigate and further improve data processing and algorithmic approaches to achieve effective and computationally efficient methods for imbalanced learning. In this paper, we have collected and reviewed 258 peer-reviewed papers from archival journals and conference papers in an attempt to provide an in-depth review of various approaches in imbalanced learning from technical and application perspectives. This work aims to provide a structured review of methods used to address the problem of imbalanced data in various domains and create a general guideline for researchers in academia or industry who want to dive into the broad field of machine learning using large-scale imbalanced data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_07917 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Review of Machine Learning Techniques in Imbalanced Data and Future Trends Jafarigol, Elaheh Trafalis, Theodore Mohammadi, Neshat Machine Learning Artificial Intelligence For over two decades, detecting rare events has been a challenging task among researchers in the data mining and machine learning domain. Real-life problems inspire researchers to navigate and further improve data processing and algorithmic approaches to achieve effective and computationally efficient methods for imbalanced learning. In this paper, we have collected and reviewed 258 peer-reviewed papers from archival journals and conference papers in an attempt to provide an in-depth review of various approaches in imbalanced learning from technical and application perspectives. This work aims to provide a structured review of methods used to address the problem of imbalanced data in various domains and create a general guideline for researchers in academia or industry who want to dive into the broad field of machine learning using large-scale imbalanced data. |
| title | A Review of Machine Learning Techniques in Imbalanced Data and Future Trends |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2310.07917 |