In machine learning, two approaches outperform traditional algorithms: ensemble learning
and deep learning. The former refers to methods that integrate multiple base models in the …

Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …

In this chapter, we explore Support Vector Machine (SVM)—a machine learning method that
has become exceedingly popular for neuroimaging analysis in recent years. Because of …

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

Ensemble learning is a prolific field in Machine Learning since it is based on the assumption
that combining the output of multiple models is better than using a single model, and it …

The aim of this study is to provide an overview the state-of-the-art elements of text
classification. For this purpose, we first select and investigate the primary and recent studies …

Mass spectrometry-based proteomic technique has become indispensable in current
exploration of complex and dynamic biological processes. Instrument development has …

Feature selection is a process aimed at filtering out unrepresentative features from a given
dataset, usually allowing the later data mining and analysis steps to produce better results …

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

Feature selection is an important preprocessing technique in the fields of data mining and
machine learning. With the promotion of privacy protection awareness, recently it becomes a …