Metaheuristic algorithms are widely known as efficient solutions for solving problems of
optimization. These algorithms supply powerful instruments with significant engineering …

Feature selection is a critical and prominent task in machine learning. To reduce the
dimension of the feature set while maintaining the accuracy of the performance is the main …

Feature selection is an important data preprocessing method. This paper studies a new multi-
objective feature selection approach, called the Binary Differential Evolution with self …

To help individuals or companies make a systematic and more accurate decisions,
sentiment analysis (SA) is used to evaluate the polarity of reviews. In SA, feature selection …

Abstract Gravitational Search Algorithm (GSA) is an optimization method inspired by the
theory of Newtonian gravity in physics. Till now, many variants of GSA have been …

Feature selection has been an important issue in machine learning and data mining, and is
unavoidable when confronting with high‐dimensional data. With the advent of multilabel …

In the real world, there are a number of optimization problems whose search space is
restricted to take binary values; however, there are many continuous metaheuristics with …

Recently mutual information based feature selection criteria have gained popularity for their
superior performances in different applications of pattern recognition and machine learning …

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 …

Feature selection is an important component of the machine learning domain, which selects
the ideal subset of characteristics relative to the target data by omitting irrelevant data. For a …