The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …

In the past two decades, significant advances have been made on automated
electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection. A number …

Abstract The Synthetic Minority Over-sampling Technique (SMOTE) is a well-known
resampling strategy that has been successfully used for dealing with the class-imbalance …

A large variety of issues influence the success of data mining on a given problem. Two
primary and important issues are the representation and the quality of the dataset …

In addition to materials, labor, equipment, and method, construction cost depends on many
other factors such as the project locality, type, construction duration, scheduling, and the …

The combination of class imbalance and overlap is currently one of the most challenging
issues in machine learning. While seminal work focused on establishing class overlap as a …

Data imbalance remains one of the most widespread problems affecting contemporary
machine learning. The negative effect data imbalance can have on the traditional learning …

Feature selection (FS) in data with class imbalance or missing values has received much
attention from researchers due to their universality in real-world applications. However, for …

Unbalanced classification is an essential machine learning task, which has attracted
widespread attention from both the academic and industrial communities due mainly to its …

The unpredictability of epileptic seizures is one of the most problematic aspects of the field of
epilepsy. Methods or devices capable of detecting seizures minutes before they occur may …