Selecting relevant feature subsets is vital in machine learning, and multiclass feature
selection is harder to perform since most classifications are binary. The feature selection …

In recent years, unsupervised feature selection methods have raised considerable interest in
many research areas; this is mainly due to their ability to identify and select relevant features …

Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …

Nowadays, the growth of the high-throughput technologies has resulted in exponential
growth in the harvested data with respect to both dimensionality and sample size. The trend …

Dimensionality reduction techniques can be categorized mainly into feature extraction and
feature selection. In the feature extraction approach, features are projected into a new space …

Organizations in many domains generate a considerable amount of heterogeneous data
every day. Such data can be processed to enhance these organizations' decisions in real …

In the medical field, distinguishing genes that are relevant to a specific disease, let's say
colon cancer, is crucial to finding a cure and understanding its causes and subsequent …

As an effective data preprocessing step, feature selection has shown its effectiveness to
prepare high-dimensional data for many machine learning tasks. The proliferation of high di …

Purpose Public sector has started exploiting artificial intelligence (AI) techniques, however,
mainly for operational but much less for tactical or level tasks. The purpose of this study is to …

Feature selection is a dimension reduction technique used to select features that are
relevant to machine learning tasks. Reducing the dataset size by eliminating redundant and …