Empirical Comparison of Approaches for Mitigating Effects of Class Imbalances in Water Quality Anomaly Detection

Empirical Comparison of Approaches for Mitigating Effects of Class Imbalances in Water Quality Anomaly Detection

Imbalanced class distribution and missing data are two common problems and occurrences in water quality anomaly detection domain. Learning algorithms in an imbalanced dataset can yield an overrated classification accuracy driven by a bias towards the majority class at the expense of the minority cla...

Full description

Bibliographic Details
Main Authors: Eustace M. Dogo, Nnamdi I. Nwulu, Bhekisipho Twala, Clinton Ohis Aigbavboa
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Class-imbalance
data preprocessing
imputation
machine learning
resampling
water quality
Online Access:https://ieeexplore.ieee.org/document/9261464/

Internet

https://ieeexplore.ieee.org/document/9261464/

Similar Items