Binary Horse herd optimization algorithm with crossover operators for feature selection
This paper proposes a binary version of Horse herd Optimization Algorithm (HOA) to tackle
Feature Selection (FS) problems. This algorithm mimics the conduct of a pack of horses …
Feature Selection (FS) problems. This algorithm mimics the conduct of a pack of horses …
Binary biogeography-based optimization based SVM-RFE for feature selection
Rapid data growth presents many challenges for Machine Learning (ML) tasks as it can
include lots of irrelevant, noisy, and redundant features. Thus, it is vital to select the most …
include lots of irrelevant, noisy, and redundant features. Thus, it is vital to select the most …
A survey on feature selection approaches for clustering
The massive growth of data in recent years has led challenges in data mining and machine
learning tasks. One of the major challenges is the selection of relevant features from the …
learning tasks. One of the major challenges is the selection of relevant features from the …
Dynamic butterfly optimization algorithm for feature selection
Feature selection represents an essential pre-processing step for a wide range of Machine
Learning approaches. Datasets typically contain irrelevant features that may negatively …
Learning approaches. Datasets typically contain irrelevant features that may negatively …
COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions
Classification of COVID-19 X-ray images to determine the patient's health condition is a
critical issue these days since X-ray images provide more information about the patient's …
critical issue these days since X-ray images provide more information about the patient's …
Feature selection based nature inspired capuchin search algorithm for solving classification problems
Identification of the optimal subset of features for Feature Selection (FS) problems is a
demanding problem in machine learning and data mining. A trustworthy optimization …
demanding problem in machine learning and data mining. A trustworthy optimization …
Multi-objective feature selection based on artificial bee colony: An acceleration approach with variable sample size
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 …
existing evolutionary feature selection algorithms have the disadvantage of high …
Artificial intelligence for diagnosis of mild–moderate COVID-19 using haematological markers
Objective The persistent spread of SARS-CoV-2 makes diagnosis challenging because
COVID-19 symptoms are hard to differentiate from those of other respiratory illnesses. The …
COVID-19 symptoms are hard to differentiate from those of other respiratory illnesses. The …
A novel hybrid wrapper–filter approach based on genetic algorithm, particle swarm optimization for feature subset selection
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 …
problems, the data sets have a high dimensionality that the existence of all features is not …
An electric fish-based arithmetic optimization algorithm for feature selection
With the widespread use of intelligent information systems, a massive amount of data with
lots of irrelevant, noisy, and redundant features are collected; moreover, many features …
lots of irrelevant, noisy, and redundant features are collected; moreover, many features …