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

Classification of Systems-I01:26

Classification of Systems-I

645
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
645
Classification of Systems-II01:31

Classification of Systems-II

540
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
540
Classification of Signals01:30

Classification of Signals

1.5K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.5K
Aggregates Classification01:29

Aggregates Classification

1.1K
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
1.1K
Force Classification01:22

Force Classification

2.5K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.5K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

449
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
449

You might also read

Related Articles

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

Sort by
Same author

Engineering of 10-Deacetylbaccatin III-10-β-O-Acetyltransferase From Taxus Species for Efficient Acetylating Non-Natural Substrates Into Taxol in Nicotiana benthamiana.

Plant biotechnology journal·2026
Same author

rTMS-induced motor cortex activation drives neural network tissueoid mediated spinal motor neural pathway reconstruction.

Theranostics·2025
Same author

Ratio Fluorescence Determination of Tetracycline with Europium(III)-Doped Boron Nitride.

Sensors (Basel, Switzerland)·2025
Same author

Ag⁺-Mediated Structural Reconstruction of a Metastable Cu<sub>35</sub> Cluster Toward Cu-Ag Heterometallic Architectures for Superior Electrocatalytic CO<sub>2</sub>-to-Ethanol Conversion.

Angewandte Chemie (International ed. in English)·2025
Same author

DON-Apt19S bioactive scaffold transplantation promotes <i>in situ</i> spinal cord repair in rats with transected spinal cord injury by effectively recruiting endogenous neural stem cells and mesenchymal stem cells.

Materials today. Bio·2025
Same author

Two-Stage Cooperation Multiobjective Evolutionary Algorithm Guided by Constraint-Sensitive Variables.

IEEE transactions on cybernetics·2025
Same journal

Poly(bromophenol blue)/CoSn(OH)<sub>6</sub> cubic particles modified pencil graphite electrode for electrochemical determination of diphenhydramine.

Scientific reports·2026
Same journal

Dietary Chlorella, Spirulina, and acidifier modulate jejunal cytokine-related gene expression in broiler chickens.

Scientific reports·2026
Same journal

Perceived physical activity barriers in university students: associations with fatigue and eating behaviours.

Scientific reports·2026
Same journal

Refuge limitation structures habitat use in agricultural landscapes: evidence from Sunda pangolins.

Scientific reports·2026
Same journal

Lightweight stateless transaction verification with outsourced witness updates for UTXO blockchains.

Scientific reports·2026
Same journal

Efficacy of historical context and exogenous features on deep learning for cooling load forecasting in chilled water plants.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Mar 5, 2026

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

8.1K

A PSO-based multi-objective multi-label feature selection method in classification.

Yong Zhang1, Dun-Wei Gong2, Xiao-Yan Sun2

  • 1School of Information and Electronic Engineering, China University of Mining and Technology, Xunzhou, 221116, China. yongzh401@126.com.

Scientific Reports
|March 25, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces an improved multi-objective particle swarm optimization (PSO) for multi-label feature selection. The novel algorithm effectively identifies optimal feature subsets, enhancing classification performance.

More Related Videos

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.7K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

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

Published on: March 1, 2024

1.4K

Related Experiment Videos

Last Updated: Mar 5, 2026

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

8.1K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.7K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

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

Published on: March 1, 2024

1.4K

Area of Science:

  • Computer Science
  • Machine Learning
  • Data Mining

Background:

  • Feature selection is crucial for multi-label classification preprocessing.
  • Existing methods often lack specific adaptations for multi-label data challenges.

Purpose of the Study:

  • To develop an advanced multi-objective particle swarm optimization (PSO) algorithm for effective multi-label feature selection.
  • To identify a Pareto set of non-dominated feature subsets for improved classification.

Main Methods:

  • An improved multi-objective PSO algorithm incorporating adaptive uniform mutation and a local learning strategy.
  • Integration of archive and crowding distance concepts to enhance Pareto set discovery.
  • Application of the algorithm to multi-label classification datasets.

Main Results:

  • The proposed PSO-based algorithm demonstrates improved performance in feature selection for multi-label classification.
  • The novel operators enhance exploration and exploitation capabilities within the search space.

Conclusions:

  • The developed algorithm offers a valuable approach for feature selection in multi-label classification tasks.
  • Experimental results validate its utility and effectiveness.