A Novel Linear Classifier for Class Imbalance Data Arising in Failure-Prone Air Pressure Systems

Abstract

An Air Pressure System (APS) is one of the crucial components of an automobile. Its failure leads to financial loses, and it may lead to loss of lives. Thus predicting such failure is a critical problem that requires a rigorous solution. Recently, many researchers have presented machine learning techniques to deal with APS failure detection. One of the major challenges in dealing with APS failure data is the presence of high class imbalance. Conventional classification criteria may not be able to efficiently handle such data. In this paper, a new machine learning method for APS failure detection is proposed. It is designed to specifically deal with the class imbalance. The method uses a linear decision boundary by maximizing Area Under the Curve (maxAUC) criterion. The proposed method was experimentally validated on an industrial dataset of APS failure. The results of the proposed method are thoroughly compared with existing linear as well as non-linear classifiers.

Keywords

• APS failure prediction
• imbalanced data
• area under the curve (AUC)
• machine learning