An Experimental Analysis of Drift Detection Methods on Multi-Class Imbalanced Data Streams

An Experimental Analysis of Drift Detection Methods on Multi-Class Imbalanced Data Streams

The performance of machine learning models diminishes while predicting the Remaining Useful Life (RUL) of the equipment or fault prediction due to the issue of concept drift. This issue is aggravated when the problem setting comprises multi-class imbalanced data. The existing drift detection methods...

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Bibliographic Details
Main Authors: Abdul Sattar Palli, Jafreezal Jaafar, Heitor Murilo Gomes, Manzoor Ahmed Hashmani, Abdul Rehman Gilal
Format: Article
Language:English
Published: MDPI AG 2022-11-01
Series:Applied Sciences
Subjects:
fault detection
concept drift
drift detection
class imbalance
multi-class classification
data stream mining
Online Access:https://www.mdpi.com/2076-3417/12/22/11688

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https://www.mdpi.com/2076-3417/12/22/11688

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