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

Bootstrapping01:24

Bootstrapping

673
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
673
Survival Tree01:19

Survival Tree

166
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
166
Associative Learning01:27

Associative Learning

605
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...
605
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

826
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
826
Introduction to Learning01:18

Introduction to Learning

551
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
551
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

2.2K
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...
2.2K

You might also read

Related Articles

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

Sort by
Same author

Beef Cattle Behavior Recognition Based on Nighttime Farm Videos via Spatio-Temporal Enhancement and Dynamic Fusion.

Animals : an open access journal from MDPI·2026
Same author

Dual-SAM/Al<sub>2</sub>O<sub>3</sub>-Nanoparticles Hole-Selective Stack With BCP/PEAI Passivation Enabling High Performance Inverted Perovskite Solar Cells.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Early supplementation with live combined <i>Bacillus subtilis</i> and <i>Enterococcus faecium</i>: association with feeding intolerance and gut microbiota composition in antibiotic-exposed preterm infants.

Frontiers in cellular and infection microbiology·2026
Same author

Misalignment Decoupling and Tilt-to-Length Suppression in a Micro-Actuated Beam Steering Mechanism via Nonlinear Cyclic Modulation.

Micromachines·2026
Same author

Tendon-Inspired, Fatigue-Resistant Conductive Organohydrogels via Solvent-Exchange-Assisted Mechanical Training.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

IL-17B protects against uropathogenic <i>E. coli</i>-induced kidney injury via macrophage infiltration modulation.

Microbiology spectrum·2026

Related Experiment Video

Updated: Sep 19, 2025

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

9.1K

Graph positive-unlabeled learning via Bootstrapping Label Disambiguation.

Chunquan Liang1, Luyue Wang2, Xinyuan Feng2

  • 1College of Information Engineering, Northwest A&F University, Shaanxi, China; Shaanxi Engineering Research Center for Intelligent Perception and Analysis of Agricultural Information, Shaanxi, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 4, 2025
PubMed
Summary

Bootstrap Label Disambiguation (BLD) enhances graph positive-unlabeled learning by treating unlabeled nodes as ambiguous. This method outperforms existing approaches and even fully labeled models in binary classification tasks.

Keywords:
Label DisambiguationPositive-Unlabeled LearningRepresentation Learning

More Related Videos

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.7K
Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

6.7K

Related Experiment Videos

Last Updated: Sep 19, 2025

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

9.1K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.7K
Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

6.7K

Area of Science:

  • Machine Learning
  • Graph Analytics
  • Data Science

Background:

  • Positive-unlabeled (PU) learning on graphs is crucial for binary classification using limited labeled data.
  • Current graph neural network methods for PU learning often use weak objective functions, limiting performance.
  • Existing methods struggle to match the performance of fully supervised approaches.

Purpose of the Study:

  • To introduce a novel method, Bootstrap Label Disambiguation (BLD), to improve graph PU learning.
  • To address the limitations of existing objective functions in PU learning.
  • To develop a method that can outperform both existing PU learning techniques and fully labeled models.

Main Methods:

  • Treating unlabeled nodes as ambiguously labeled (both positive and negative).
  • Implementing a Bootstrap Label Disambiguation (BLD) strategy for progressive label ambiguity resolution.
  • Utilizing a node representation learning module with bootstrapping and a central-region-based disambiguation strategy.

Main Results:

  • BLD significantly outperforms existing graph PU learning methods.
  • In many cases, BLD surpasses the performance of fully labeled classification models.
  • The method effectively resolves label ambiguities and transforms them into valuable supervision.

Conclusions:

  • BLD offers a powerful new approach to graph positive-unlabeled learning.
  • The method demonstrates superior performance across various real-world datasets.
  • BLD effectively handles label ambiguity, leading to highly accurate binary classification.