Rolling element bearing failure is one of the foremost causes of breakdown in rotating machinery. It is not uncommon to replace a defected/used bearing with a new one that has shorter remaining useful life than the defected one. Thus, prognostics of bearing plays critical role for increased availability and reduced cost. Effective prognostics highly depend on the quality of the extracted featur...

Existing works show that the rotation forest algorithm has competitive performance in terms of classification accuracy for gene expression data. However, most existing works only focus on the classification accuracy and neglect the classification costs. In this study, we propose a cost-sensitive rotation forest algorithm for gene expression data classification. Three classification costs, namel...

The classification of Internet-of-Things (IoT) devices into different types is paramount importance, from multiple perspectives, including security and privacy aspects. Recent works have explored machine learning techniques for fingerprinting (or classifying) IoT devices, with promising results. However, the existing assumed that features used building models are readily available or can be eas...

Abstract Class imbalance poses a major challenge for machine learning as most supervised models might exhibit bias towards the majority class and under-perform in minority class. Cost-sensitive tackles this problem by treating classes differently, formulated typically via user-defined fixed misclassification cost matrix provided input to learner. Such parameter tuning is challenging task that r...