Feature selection, the process of identifying relevant features to be incorporated into a
proposed model, is one of the significant steps of the learning process. It removes noise from …

One of the major problems in microarray datasets is the large number of features, which
causes the issue of “the curse of dimensionality” when machine learning is applied to these …

Datasets often include excessive or irrelevant data that affect the performance and
complexity of the machine learning model. Feature selection is one of the most effective …

Microarray data technology offers a great opportunity for the early diagnosis of diseases.
However, the classification of microarray data can be difficult for machine learning classifiers …

The classification is one of the main technique of machine learning science. In many
problems, the data sets have a high dimensionality that the existence of all features is not …

Huge volume of dataset with extremely high dimension are emerging in a notable variety of
fields, going from bioinformatics to text mining, which gives rise to a crucial technique …

One major component of machine learning is feature analysis which comprises of mainly
two processes: feature selection and feature extraction. Due to its applications in several …

It is well known that the performance of most data mining algorithms can be deteriorated by
features that do not add any value to learning tasks. Feature selection can be used to limit …

During past few decades, researchers worked on data preprocessing techniques for the
datasets. Data preprocessing techniques are needed, where the data are prepared for …

Classification of data crosses different domains has been extensively researched and is one
of the basic methods for distinguishing one from another, as we need to know which belongs …