Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Decision Making: P-value Method01:09

Decision Making: P-value Method

6.6K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.6K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

6.0K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
6.0K
Cluster Sampling Method01:20

Cluster Sampling Method

13.8K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.8K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.0K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
5.0K
Frequency-dependent Selection01:21

Frequency-dependent Selection

22.8K
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.
22.8K
Optimal Foraging00:48

Optimal Foraging

13.0K
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.
13.0K

You might also read

Related Articles

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

Sort by
Same author

A V<sub>2</sub>CT<sub>x</sub>/V<sub>2</sub>O<sub>5</sub>/SnO<sub>2</sub> Ternary Heterostructure<i>-</i>Based Gas Sensor for Highly Selective Detection of Electrolyte Leakage in a Lithium<i>-</i>Ion Battery.

ACS sensors·2026
Same author

A Multiobjective Evolutionary Algorithm Based on Bipopulation With Uniform Sampling for Neural Architecture Search.

IEEE transactions on neural networks and learning systems·2026
Same author

A Time-Division-Based Constrained Multiobjective Optimization Method for Coal Mine Integrated Energy System Dispatch Problem.

IEEE transactions on cybernetics·2026
Same author

Model-free and finite-time sliding-mode tracking control based on a second-order adaptive disturbance observer.

ISA transactions·2025
Same author

Dynamic Ensemble Selection for Imbalanced Data Streams With Concept Drift.

IEEE transactions on neural networks and learning systems·2022
Same author

Multisurrogate-Assisted Multitasking Particle Swarm Optimization for Expensive Multimodal Problems.

IEEE transactions on cybernetics·2021
Same journal

Relaxed Stability Conditions for Model Predictive Control of Hybrid Dynamical Systems Using Hybrid Recurrent Neural Networks.

IEEE transactions on cybernetics·2026
Same journal

An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models.

IEEE transactions on cybernetics·2026
Same journal

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026
Same journal

Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

IEEE transactions on cybernetics·2026
Same journal

A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

IEEE transactions on cybernetics·2026
Same journal

Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Dec 9, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

Multiobjective Particle Swarm Optimization for Feature Selection With Fuzzy Cost.

Ying Hu, Yong Zhang, Dunwei Gong

    IEEE Transactions on Cybernetics
    |September 14, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a fuzzy multiobjective feature selection (FS) method using particle swarm optimization (PSO) to address limitations in traditional FS techniques. The new method effectively handles fuzzy feature costs, improving performance in approximation, diversity, and cost.

    More Related Videos

    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
    11:53

    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

    Published on: December 9, 2012

    13.3K
    A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
    13:54

    A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

    Published on: August 18, 2023

    5.5K

    Related Experiment Videos

    Last Updated: Dec 9, 2025

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    7.9K
    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
    11:53

    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

    Published on: December 9, 2012

    13.3K
    A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
    13:54

    A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

    Published on: August 18, 2023

    5.5K

    Area of Science:

    • Machine Learning
    • Data Mining
    • Computational Intelligence

    Background:

    • Traditional feature selection (FS) methods assume precise feature costs, limiting real-world applications.
    • Handling uncertainty in feature costs is crucial for robust machine learning models.

    Purpose of the Study:

    • To propose a fuzzy multiobjective feature selection (FS) method using particle swarm optimization (PSO) to address the challenge of fuzzy feature costs.
    • To develop a novel approach that incorporates fuzzy dominance and fuzzy crowding distance for improved FS.

    Main Methods:

    • A fuzzy multiobjective FS method (PSOMOFS) utilizing particle swarm optimization.
    • Development of a fuzzy dominance relationship for particle comparison.
    • Introduction of a fuzzy crowding distance measure for archive pruning and leader selection.
    • Inclusion of a tolerance coefficient to align Pareto-optimal solutions with decision-maker preferences.

    Main Results:

    • PSOMOFS was evaluated on UCI datasets, demonstrating superior performance compared to existing fuzzy and typical multiobjective FS methods.
    • The method achieved improved results in approximation, diversity, and feature cost.
    • Experimental validation confirmed the effectiveness of the fuzzy dominance and crowding distance measures.

    Conclusions:

    • The proposed PSOMOFS method offers an effective solution for feature selection problems with fuzzy costs.
    • The integration of fuzzy concepts enhances the adaptability and performance of multiobjective evolutionary algorithms in FS.
    • This research contributes a valuable tool for machine learning practitioners dealing with uncertain feature cost information.