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 …

Specialized data preparation techniques, ranging from data cleaning, outlier detection,
missing value imputation, feature selection (FS), amongst others, are procedures required to …

An original swarm-based, bio-inspired metaheuristic algorithm, named electric eel foraging
optimization (EEFO) is developed and tested in this work. EEFO draws inspiration from the …

In the past decades, the rapid growth of computer and database technologies has led to the
rapid growth of large-scale datasets. On the other hand, data mining applications with high …

One of the major problems in Big Data is a large number of features or dimensions, which
causes the issue of “the curse of dimensionality” when applying machine learning …

Feature selection is a required preprocess stage in most of the data mining tasks. This paper
presents an improved Harris hawks optimization (HHO) to find high-quality solutions for …

Feature selection, an optimization problem, becomes an important pre-process tool in data
mining, which simultaneously aims at minimizing feature-size and maximizing model …

Searching for the (near) optimal subset of features is a challenging problem in the process of
feature selection (FS). In the literature, Swarm Intelligence (SI) algorithms show superior …

Feature selection (FS) is an important preprocessing technique for dimensionality reduction
in classification problems. Particle swarm optimization (PSO) algorithms have been widely …

In the past decades, the rapid growth of computer and database technologies has led to the
rapid growth of large-scale medical datasets. On the other, medical applications with high …