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Prediction performance improvement for highly imbalanced monitoring data

Li, Yuhua, Maguire, Liam, McCann, Michael and Johnston, Adrian (2010) Prediction performance improvement for highly imbalanced monitoring data. In: The 7th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, Stratford-upon-Avon. British Institute of Non-Destructive Testing. 8 pp. [Conference contribution]

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In engineering applications, we often face highly imbalanced data problems where majority of the data are from a condition and small minority are from others. Directly learning classifier on such problems would be prone to a biased classification performance by the majority class, so resulting in poor predication on the minority class. This paper proposes a method for balancing training data, which over-samples the minority class. The method uses between-class and within-class information to decide the vicinity space of an example. It generates synthetic examples along orthogonal directions in the vicinity, so it ensures the generated synthetic examples well represent the entire vicinity space and be more similar to minority class than majority class. The method is easy to use, as it involves no parameter setting. A real world problem of semiconductor manufacturing line monitoring and process control data is used to demonstrate that classification performance can be significantly improved through learning on balanced data by the proposed method.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:16140
Deposited By: Dr Yuhua Li
Deposited On:29 Oct 2010 08:24
Last Modified:29 Oct 2010 08:24

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