Disease diagnosis and prediction methods in biotechnology and medicine have significantly
advanced over time. Consequently, analyzing raw gene expression is crucial for identifying …

Several feature selection methods have been developed to extract the optimal features from
a dataset in medical datasets classification. Creating an efficient technique has become a …

Introduction: In pattern recognition and data mining, feature selection is one of the most
crucial tasks. To increase the efficacy of classification algorithms, it is necessary to identify …

Introduction The main feature selection methods include filter, wrapper-based, and
embedded methods. Because of its characteristics, the wrapper method must include a …

Microarray data represents a valuable tool for the identification of biomarkers associated
with diseases and other biological conditions. Genes, in particular, are a type of biomarker …

According to the WHO, Cancer is a prominent cause of mortality worldwide, accounting for~
10 million fatalities at the end of 2020. The most common types of cancers include Lung …

Modern data collection technologies may produce thousands of or even more features in a
single dataset. The high dimensionality of data poses a barrier to determining discriminating …

Feature selection (FS) is a popular data pre-processing technique in machine learning to
extract the optimal features to maintain or increase the classification accuracy of the dataset …

The feature selection problem is one of the pre-processing mechanisms to find the optimal
subset of features from a dataset. The search space of the problem will exponentially grow …

The identification of tumors through gene analysis in microarray data is a pivotal area of
research in artificial intelligence and bioinformatics. This task is challenging due to the large …