Class imbalance is one of the challenges of machine learning and data mining fields.
Imbalance data sets degrades the performance of data mining and machine learning …

The imbalanced data problems in data mining are common nowadays, which occur due to
skewed nature of data. These problems impact the classification process negatively in …

Obtaining sufficient balanced data is tricky in practical rotating machinery fault diagnosis
tasks. It is a pressing real-world problem to accurately diagnose faults from imbalanced data …

Due to the real working conditions and data acquisition equipment, the collected working
data of bearings are actually limited. Meanwhile, as the rolling bearing works in the normal …

The fault diagnosis of rolling bearings with imbalanced data has always been a particularly
challenging problem. With data augmentation methods to complement the imbalanced …

Data preprocessing is a major and essential stage whose main goal is to obtain final data
sets that can be considered correct and useful for further data mining algorithms. This paper …

As the risk of diseases diabetes and hypertension increases, machine learning algorithms
are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction …

Undersampling is a widely adopted method to deal with imbalance pattern classification
problems. Current methods mainly depend on either random resampling on the majority …

Solar power is an important renewable energy resource and acts as a major contributor to
replacing fossil fuel generators and reducing carbon emissions. However, the intermittent …

Diagnosis of bearings generally plays an important role in fault diagnosis of mechanical
system, and machine learning has been a promising tool in this field. In many real …