We summarise various ways of performing dimensionality reduction on high‐dimensional
microarray data. Many different feature selection and feature extraction methods exist and …

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

Nowadays, microarray data processing is one of the most important applications in
molecular biology for cancer diagnosis. A major task in microarray data processing is gene …

The emergence of DNA Microarray technology has enabled researchers to analyze the
expression level of thousands of genes simultaneously. The Microarray data analysis is the …

Recent advances in single-cell biotechnologies have resulted in high-dimensional datasets
with increased complexity, making feature selection an essential technique for single-cell …

Ensemble learning is an intensively studied technique in machine learning and pattern
recognition. Recent work in computational biology has seen an increasing use of ensemble …

Ensemble methods try to improve performance via integrating different kinds of input data,
features, or learning algorithms. In addition to other areas, they are finding their applications …

To further improve fault diagnosis accuracy, a new hybrid feature selection approach
combined with a genetic algorithm (GA) and support vector machine (SVM) is presented in …

Gene expression profiles, which represent the state of a cell at a molecular level, have great
potential as a medical diagnosis tool. In cancer classification, available training data sets are …

Empirical and process-based models are currently used to predict crop yield at field and
regional levels. A mechanistic model named STICS (Multidisciplinary Simulator for Standard …