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 …

The evaluation of feature selection methods for text classification with small sample datasets
must consider classification performance, stability, and efficiency. It is, thus, a multiple …

Feature selection (FS) represents an optimization problem that aims to simplify and improve
the quality of highly dimensional datasets through selecting prominent features and …

Automated classification of text into predefined categories has always been considered as a
vital method to manage and process a vast amount of documents in digital forms that are …

Feature selection is an important data-preprocessing technique in classification problems
such as bioinformatics and signal processing. Generally, there are some situations where a …

Classification of COVID-19 X-ray images to determine the patient's health condition is a
critical issue these days since X-ray images provide more information about the patient's …

Feature selection is known as a good solution to the high dimensionality of the feature space
and mostly preferred feature selection methods for text classification are filter-based ones. In …

As the huge dimensionality of textual data restrains the classification accuracy, it is essential
to apply feature selection (FS) methods as dimension reduction step in text classification …

With the extensive applicability of machine learning classification algorithms to a wide
spectrum of domains, feature selection (FS) becomes a relevant data preprocessing …

Due to the influence of environment, data obtained in real world are not completely reliable
sometimes. This paper focuses on tackling the feature selection problem with unreliable …