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

366
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:
366
Classification of Systems-II01:31

Classification of Systems-II

259
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,
259
Aggregates Classification01:29

Aggregates Classification

416
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...
416
Associative Learning01:27

Associative Learning

682
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
682
Methods of Classification and Identification01:28

Methods of Classification and Identification

338
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
338
Classification of Signals01:30

Classification of Signals

1.0K
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...
1.0K

You might also read

Related Articles

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

Sort by
Same author

Development and evaluation of an experimental inactivated vaccine against lumpy skin disease.

Veterinary world·2025
Same author

Understanding hate speech: the HateInsights dataset and model interpretability.

PeerJ. Computer science·2024
Same author

Selection criteria of image reconstruction algorithms for terahertz short-range imaging applications.

Optics express·2022
Same author

Green Synthesis and Investigation of Surface Effects of α-Fe<sub>2</sub>O<sub>3</sub>@TiO<sub>2</sub> Nanocomposites by Impedance Spectroscopy.

Materials (Basel, Switzerland)·2022
Same author

A Low-Cost Metamaterial Sensor Based on DS-CSRR for Material Characterization Applications.

Sensors (Basel, Switzerland)·2022
Same author

Exposure of broiler chickens to chronic heat stress increases the severity of white striping on the pectoralis major muscle.

Tropical animal health and production·2021
Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026
Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026
Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026
Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026
Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026
Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·2026
See all related articles

Related Experiment Video

Updated: Oct 18, 2025

Visual Classical Conditioning in Wood Ants
05:46

Visual Classical Conditioning in Wood Ants

Published on: October 5, 2018

8.5K

Semi-supervised associative classification using ant colony optimization algorithm.

Hamid Hussain Awan1, Waseem Shahzad1

  • 1Department of Computer Science, National Unibersity of Computer and Emerging Sciences Islamabad, Islamabad, Pakistan.

Peerj. Computer Science
|October 4, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces Self-Training using Associative Classification with Ant Colony Optimization (ST-AC-ACO) to improve classification accuracy. This novel approach effectively labels unlabeled data, outperforming traditional self-training methods.

Keywords:
Ant colony optimizationAssociative classificationClassificationData miningPseudo labelingSekf-trainingSemi-supervised learning

More Related Videos

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

7.7K
Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.
10:14

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.

Published on: December 12, 2012

10.7K

Related Experiment Videos

Last Updated: Oct 18, 2025

Visual Classical Conditioning in Wood Ants
05:46

Visual Classical Conditioning in Wood Ants

Published on: October 5, 2018

8.5K
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

7.7K
Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.
10:14

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.

Published on: December 12, 2012

10.7K

Area of Science:

  • Machine Learning
  • Data Science

Background:

  • Labeled data is crucial for classification but often scarce or expensive.
  • Traditional self-training methods struggle with accuracy when most data is unlabeled.

Purpose of the Study:

  • To address the limitations of traditional self-training in scenarios with limited labeled data.
  • To enhance classification accuracy by effectively labeling unlabeled instances.

Main Methods:

  • Proposed a novel approach: Self-Training using Associative Classification with Ant Colony Optimization (ST-AC-ACO).
  • Leveraged Ant Colony Optimization (ACO) to build associative classification rules using labeled and pseudo-labeled data.
  • Exploited attribute value associations and term-to-class label relationships.

Main Results:

  • The ST-AC-ACO approach demonstrated superior performance compared to traditional self-training methods.
  • Successfully improved classification accuracy by effectively labeling unlabeled data instances.

Conclusions:

  • The proposed ST-AC-ACO method offers a more effective solution for semi-supervised learning tasks.
  • Associative classification combined with ant colony optimization significantly enhances self-training capabilities.