Imbalanced Classification for Fault Detection in Monitored Critical Infrastructures



Yan-Fu Li


Safety and reliability are among the most crucial factors for the critical infrastructures (CIs). For this reason, they are typically closely monitored and large amounts of data have been collected. Due to their importance, CIs are designed to be highly reliable such that fault cases are rare in the Bigdata set. This renders the fault detection an imbalanced binary classification task. In this work, we developed accurate data mining classifier to tackle this problem. The imbalance ratio of the data can be more than 200.

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