Mitigating Multicollinearity in Induction Motors Fault Diagnosis Through Hierarchical Clustering-Based Feature Selection

Mitigating Multicollinearity in Induction Motors Fault Diagnosis Through Hierarchical Clustering-Based Feature Selection

This paper addresses the challenge of multicollinearity among input features in induction motor (IM) fault diagnosis, which often degrades the performance and reliability of machine learning classifiers. A novel feature selection approach based on agglomerative hierarchical clustering (AHC) is propo...

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Bibliographic Details
Main Authors: Bassam A. Hemade, Sabbah Ataya, Attia A. El-Fergany, Nader M. A. Ibrahim
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Applied Sciences
Subjects:
agglomerative hierarchical clustering
artificial neural networks
fault diagnosis
feature selection
multicollinearity
random forest
Online Access:https://www.mdpi.com/2076-3417/15/13/7012
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