Feature Selection for Imbalanced Fault Diagnosis
There are many labeled data sets which have an unbalanced representation among the classes in them.When the imbalance is large,classification accuracy on the smaller class tends to be lower.In particular,when a class is of great interest but occurs relatively rarely such as cases of fraud,instances of disease,and so on,it is important to accurately identify it.Fault diagnosis on diesel engine is a difficult problem due to the complex structure of the engines and the presence of multi-excite sources.Class imbalance problem is also 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 diesel engine.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 selection for EasyEnsemble) to solve the class imbalanced problem in the fault diagnosis of diesel engine.Experimental results on UCI data sets and diesel engine data set show that PREE improves the classification performance and prediction ability on the imbalanced dataset.