Cargando…
Tomek Link and SMOTE Approaches for Machine Fault Classification with an Imbalanced Dataset
Data-driven methods have prominently featured in the progressive research and development of modern condition monitoring systems for electrical machines. These methods have the advantage of simplicity when it comes to the implementation of effective fault detection and diagnostic systems. Despite th...
Autores principales: | Swana, Elsie Fezeka, Doorsamy, Wesley, Bokoro, Pitshou |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9099503/ https://www.ncbi.nlm.nih.gov/pubmed/35590937 http://dx.doi.org/10.3390/s22093246 |
Ejemplares similares
-
Research on expansion and classification of imbalanced data based on SMOTE algorithm
por: Wang, Shujuan, et al.
Publicado: (2021) -
SMOTE for high-dimensional class-imbalanced data
por: Blagus, Rok, et al.
Publicado: (2013) -
A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare
por: Kosolwattana, Tanapol, et al.
Publicado: (2023) -
Effective treatment of imbalanced datasets in health care using modified SMOTE coupled with stacked deep learning algorithms
por: Sowjanya, A. Mary, et al.
Publicado: (2022) -
A Virtual Sensing Concept for Nitrogen and Phosphorus Monitoring Using Machine Learning Techniques
por: Paepae, Thulane, et al.
Publicado: (2022)