Feature selection is an important step in data mining and is used in various domains
including genetics, medicine, and bioinformatics. Choosing the important features (genes) is …

Ensemble classification is a well-established approach that involves fusing the decisions of
multiple predictive models. A similar “ensemble logic” has been recently applied to …

Univariate feature rankers have been frequently used to order genes (features) in terms of
their importance to a given bioinformatics challenge. Unfortunately, the resulting feature …

Dimensionality reduction techniques have become a required step when working with
bioinformatics datasets. Techniques such as feature selection have been known to not only …

Feature selection is an important preprocessing step when learning from bioinformatics
datasets. Since these datasets often have high dimensionality (a large number of features) …

Finding a good predictive model for a high‐dimensional data set can be challenging. For
genetic data, it is not only important to find a model with high predictive accuracy, but it is …

Feature (gene) selection is an important preprocessing step for performing data mining on
large-scale bioinformatics datasets. However, one known concern is that feature selection …

Abstract Study Objectives Heart rate variability (HRV)-based machine learning models hold
promise for real-world vigilance evaluation, yet their real-time applicability is limited by …

One of the more prevalent problems when working with bioinformatics datasets is class
imbalance, when there are more instances in one class compared to the other class (es) …

Ensemble feature selection has recently become a topic of interest for researchers,
especially in the area of bioinformatics. The benefits of ensemble feature selection include …