Research on Feature Selection for Imbalanced Problem from Fault Diagnosis on Gear

Abstract:

Defect is one of the important factors resulting in gear fault, so it is significant to study the technology of defect diagnosis for gear. Class imbalance problem is encountered in the fault diagnosis, which causes seriously negative effect on the performance of classifiers that assume a balanced distribution of classes. Though it is critical, few previous works paid attention to this class imbalance problem in the fault diagnosis of gear. In imbalanced problems, some features are redundant and even irrelevant. These features will hurt the generalization performance of learning machines. Here we propose PREE (Prediction Risk based feature selectionfor EasyEnsemble) to solve the class imbalanced problem in the fault diagnosis of gear. Experimental results on UCI data sets and gear data set show that PREE improves the classification performance and prediction ability on the imbalanced dataset.

• Periodical: Advanced Materials Research (Volumes 466-467)
• Pages: 886-890
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.466-467.886
• Online since: February 2012
• Authors: Tian Yu Liu
• Keywords: Ensemble Learning, Fault Diagnosis, Gear, Imbalanced Data Sets

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