Recently, feature selection and dimensionality reduction have become fundamental tools for
many data mining tasks, especially for processing high-dimensional data such as gene …

Microarray data classification is a difficult challenge for machine learning researchers due to
its high number of features and the small sample sizes. Feature selection has been soon …

Explainability has become one of the most discussed topics in machine learning research in
recent years, and although a lot of methodologies that try to provide explanations to black …

Plenty of feature selection methods are available in literature due to the availability of data
with hundreds of variables leading to data with very high dimension. Feature selection …

As datasets continue to increase in size, it is important to select the optimal feature subset
from the original dataset to obtain the best performance in machine learning tasks. Highly …

Support vector machine recursive feature elimination (SVM-RFE) is a powerful feature
selection algorithm. However, when the candidate feature set contains highly correlated …

We present a comprehensive experimental study of 12 individual as well as 6 ensemble
methods for feature selection for classification tasks on environmental data, more specifically …

Rolling bearing defects in induction motors are usually diagnosed using vibration signal
analysis. For accurate detection of rolling bearing defects, appropriate feature extraction …

Feature selection aims to eliminate unimportant and redundant features or to select effective
and interacting features. It is a challenging task to accurately measure the relationships of …

Motivation DNA replication is a key step to maintain the continuity of genetic information
between parental generation and offspring. The initiation site of DNA replication, also called …