Maximizing the classification accuracy and minimizing the number of selected features are
two primary objectives in feature selection, which is inherently a multiobjective task …

Abstract Recent advancements in Information Technology (IT) have engendered the rapid
production of big data, as enormous volumes of data with high dimensional features grow …

Several feature selection methods have been developed to extract the optimal features from
a dataset in medical datasets classification. Creating an efficient technique has become a …

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 …

Feature selection aims at finding the minimum number of features that result in high
classification accuracy. Accordingly, the feature selection is considered as a multi-objective …

Feature selection is a pre-processing procedure of choosing the optimal feature subsets for
constructing model, yet it is difficult to satisfy the requirements of reducing number of …

Background and objective Medical data plays a decisive role in disease diagnosis. The
classification accuracy of high-dimensional datasets is often diminished by several …

Due to the need to repeatedly call a classifier to evaluate individuals in the population,
existing evolutionary feature selection algorithms have the disadvantage of high …

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 …