This systematic review provides researchers interested in feature selection (FS) for
processing microarray data with comprehensive information about the main research …

Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …

For the DNA microarray datasets, tumor classification based on gene expression profiles
has drawn great attention, and gene selection plays a significant role in improving the …

Multi-label learning is a machine learning subclass that aims to assign more than one label
simultaneously for each instance. Many real-world tasks include high-dimensional data …

Identification of biomarkers is essential for the diagnosis and prognosis of certain diseases,
like cancer. Gene selection purpose is finding the minimum number of genes that can …

Retrieving the relevant information from the high‐dimensional dataset enhances the
classification accuracy of a predictive model. This research critique has devised an …

The microarray cancer gene expression data is used for classifying the cancer disease. This
work focuses on two objectives, first is the cancer gene feature selection by employing a …

The Cancer Feature Selection and classification problem is one of the prevalent tasks in
computational molecular biology. Detecting a gene or list of genes which cause cancer can …

Cancer classification is one of the crucial tasks in medical field. The gene expression of cells
helps in identifying the cancer. The high dimensionality of gene expression data hinders the …

Modified graph clustering ant colony optimization (MGCACO) algorithm is an unsupervised
feature selection (UFS) algorithm used in determining a subset of effective genes from …