Real-world classification problems typically exhibit an imbalanced or long-tailed label
distribution, wherein many labels are associated with only a few samples. This poses a …

This study proposes a new model which is fully specified for automated seizure onset
detection and seizure onset prediction based on electroencephalography (EEG) …

Background Variable selection on high throughput biological data, such as gene expression
or single nucleotide polymorphisms (SNPs), becomes inevitable to select relevant …

Variable selection plays an important role in high dimensional statistical modelling which
nowadays appears in many areas and is key to various scientific discoveries. For problems …

The ability of computational intelligence methods to predict epileptic seizures is evaluated in
long-term EEG recordings of 278 patients suffering from pharmaco-resistant partial epilepsy …

Background Conduct disorder (CD) is a common syndrome with far-reaching effects. Risk
factors for the development of CD span social, psychological, and biological domains …

Class imbalance situations, where one class is rare compared to the other, arise frequently
in machine learning applications. It is well known that the usual misclassification error is ill …

The visual analysis of common neurological disorders such as epileptic seizures in
electroencephalography (EEG) is an oversensitive operation and prone to errors, which has …

We present a probabilistic method for linking multiple datafiles. This task is not trivial in the
absence of unique identifiers for the individuals recorded. This is a common scenario when …

While Distance Weighted Discrimination (DWD) is an appealing approach to classification in
high dimensions, it was designed for balanced datasets. In the case of unequal costs …