Evaluating classifier performance with highly imbalanced Big Data

Evaluating classifier performance with highly imbalanced Big Data

Abstract Using the wrong metrics to gauge classification of highly imbalanced Big Data may hide important information in experimental results. However, we find that analysis of metrics for performance evaluation and what they can hide or reveal is rarely covered in related works. Therefore, we addre...

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Bibliographic Details
Main Authors: John T. Hancock, Taghi M. Khoshgoftaar, Justin M. Johnson
Format: Article
Language:English
Published: SpringerOpen 2023-04-01
Series:Journal of Big Data
Subjects:
Extremely randomized trees
XGBoost
Class imbalance
Big Data
Undersampling
AUC
Online Access:https://doi.org/10.1186/s40537-023-00724-5

Internet

https://doi.org/10.1186/s40537-023-00724-5

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