Learning From High-Dimensional Biomedical Datasets: The Issue of Class Imbalance

Learning From High-Dimensional Biomedical Datasets: The Issue of Class Imbalance

As witnessed by a vast corpus of literature, dimensionality reduction is a fundamental step for biomedical data analysis. Indeed, in this domain, there is often the need for coping with a huge number of data attributes (or features). By removing irrelevant or redundant attributes, feature selection...

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Bibliographic Details
Main Author: Barbara Pes
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Bioinformatics
class imbalance
cost-sensitive classification
feature selection
high-dimensional data analysis
random forest
Online Access:https://ieeexplore.ieee.org/document/8957486/

Internet

https://ieeexplore.ieee.org/document/8957486/

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