In the recent decades, researchers have introduced an abundance of feature selection
methods many of which are studied and analyzed over the high dimensional datasets …

Feature selection is one of the important techniques of dimensionality reduction in data
preprocessing because datasets generally have redundant and irrelevant features that …

Feature selection (FS) is commonly thought of as a pre-processing strategy for determining
the best subset of characteristics from a given collection of features. Here, a novel discrete …

The aim of any data mining technique is to build an efficient predictive or descriptive model
of a large amount of data. Applications of evolutionary algorithms have been found to be …

Sales forecasting uses historical sales figures, in association with products characteristics
and peculiarities, to predict short-term or long-term future performance in a business, and it …

Cervical cancer is one of the vital and most frequent cancers, but can be cured effectively if
diagnosed in the early stage. This is a novel effort towards effective characterization of cervix …

Medical datasets frequently include vast feature sets with numerous features that are related
to one another. As a result, the curse of dimensionality affects learning from a medical …

Purpose–The purpose of this paper is to tune support vector machine (SVM) classifier using
grey wolf optimizer (GWO). Design/methodology/approach–The schema of the work aims at …

This paper starts with two hypotheses. The first one is that the simultaneous optimization of
the hyperparameters regulating the classifier within a wrapper method, while the best subset …

Searching for robust and discriminative features is challenging because of the high-
dimensional characteristics of features. Although numerous dimension-reduction …