Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Quantifying and Rejecting Outliers: The Grubbs Test

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...

Gradient and Del Operator

Gradient and Del Operator

In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a vector...

Hybrid Zones

Hybrid Zones

Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.

Frequency-dependent Selection

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.

Types of Selection

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...

Optimal Foraging

Optimal Foraging

How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Genetic Artificial Hummingbird Algorithm-Support Vector Machine for Timely Power Theft Detection.

TheScientificWorldJournal·2024

Same author

An Efficient PCA-GA-HKSVM-Based Disease Diagnostic Assistant.

BioMed research international·2021

Same author

A Master-Slave Binary Grey Wolf Optimizer for Optimal Feature Selection in Biomedical Data Classification.

BioMed research international·2021

Same author

An Innovative Excited-ACS-IDGWO Algorithm for Optimal Biomedical Data Feature Selection.

BioMed research international·2020

Same author

Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis.

BioMed research international·2020

Search research articles

Related Experiment Video

Updated: Oct 21, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Hybrid Gradient Descent Grey Wolf Optimizer for Optimal Feature Selection.

Peter Mule Kitonyi¹, Davies Rene Segera¹

¹Department of Electrical and Information Engineering, University of Nairobi, Nairobi 30197, Kenya.

Biomed Research International

|September 9, 2021

Summary

This study introduces a novel optimizer combining grey wolf and gradient descent algorithms for effective feature selection. The new method shows promise in reducing data complexity and improving model performance on medical datasets.

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Related Experiment Videos

Last Updated: Oct 21, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Area of Science:

Computer Science
Machine Learning
Optimization Algorithms

Background:

Feature selection is crucial for reducing dimensionality in large datasets, enhancing model efficiency and accuracy.
Existing methods often struggle to balance feature subset size with predictive performance.
Metaheuristic algorithms like the grey wolf algorithm offer potential but require integration with gradient-based methods for optimization.

Purpose of the Study:

To design and evaluate a hybrid optimizer merging the grey wolf algorithm and gradient descent for feature selection.
To assess the proposed optimizer's performance against established algorithms on benchmark functions and real-world medical datasets.
To investigate the optimizer's ability to optimize feature subsets for improved accuracy and reduced complexity.

Main Methods:

A novel hybrid optimizer was developed by integrating the grey wolf algorithm (metaheuristic) with gradient descent (gradient-based).
The hybrid optimizer was initially validated on 23 continuous test functions against the original grey wolf algorithm.
Binary implementations were created for feature selection and compared on six UCI medical datasets against binary grey wolf and binary grey wolf particle swarm optimizers.

Main Results:

The proposed hybrid optimizer demonstrated superior performance in 3 out of 6 medical datasets based on average metrics.
Comparative analysis showed the hybrid approach's effectiveness in feature selection tasks.
The optimizer exhibited a promising balance between feature reduction and classification accuracy.

Conclusions:

The hybrid grey wolf-gradient descent optimizer is a viable approach for effective feature selection.
The proposed method shows potential for enhancing machine learning model performance by optimizing feature subsets.
Further research can explore enhancements to further improve the optimizer's capabilities in complex feature selection scenarios.