Feature selection technique is a knowledge discovery tool which provides an understanding
of the problem through the analysis of the most relevant features. Feature selection aims at …

This paper surveys main principles of feature selection and their recent applications in big
data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and …

Feature selection algorithm contributes a lot in the domain of medical diagnosis. Choosing a
small subset of genes that enable a classifier to predict the presence or type of disease …

Gene selection is a demanding task for microarray data analysis. The diverse complexity of
different cancers makes this issue still challenging. In this study, a novel evolutionary …

Microarray technology has become an emerging trend in the domain of genetic research in
which many researchers employ to study and investigate the levels of genes' expression in a …

Identifying the informative genes has always been a major step in microarray data analysis.
The complexity of various cancer datasets makes this issue still challenging. In this paper, a …

Classifying data samples into their respective categories is a challenging task, especially
when the dataset has more features and only a few samples. A robust model is essential for …

Feature selection refers to the problem of finding the optimal subset of features by removing
irrelevant and redundant features to improve classification accuracy. The determination of …

Data analytics is routinely used to support biomedical research in all areas, with particular
focus on the most relevant clinical conditions, such as cancer. Bioinformatics approaches, in …

In this paper, a novel adaptive filtering algorithm combining both affine projection (AP)
method and robust mixed-norm algorithm (RMNA) is proposed, which is called APRMNA …