Feature selection techniques are widely being used as a preprocessing step to train
machine learning algorithms to circumvent the curse of dimensionality, overfitting, and …

The identification of informative and distinguishing features from high-dimensional data has
gained significant attention in the field of machine learning. Recently, there has been …

In recent years, the increasing global demand for mushrooms has made the enhancement of
mushroom yield a focal point of research. Currently, the primary methods for developing …

The performance of machine learning algorithms can be affected by redundant features of
high-dimensional data. Furthermore, these irrelevant features increase the time of …

In recent years, gene expression data analysis has gained growing significance in the fields
of machine learning and computational biology. Typically, microarray gene datasets exhibit …

Feature selection (FS) is a crucial data preprocessing technique that selects important
features to enhance learning efficiency, yet it encounters challenges due to the high …

Identifying the most relevant variables or features in massive datasets for dimensionality
reduction can lead to improved and more informative display, faster computation times, and …

Abstract Knowledge discovery from big datasets is one of the most important challenges in
the pattern recognition field. More important than this is how much the extracted information …

High-dimensional problems have increasingly drawn attention in gene selection and
analysis. To add insult to injury, usually the number of features is greater than number of …

Prostate cancer remains a complex and challenging disease, necessitating innovative
approaches for prognosis and therapeutic guidance. This study integrates machine learning …