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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

38
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
38
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

23
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
23
Synthetic Biology02:55

Synthetic Biology

4.7K
Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
4.7K
Molecular Models02:00

Molecular Models

37.7K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
37.7K

You might also read

Related Articles

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

Sort by
Same author

LwHM: lightweight hybrid classifier for SDN-attack detection using recursive feature elimination.

Scientific reports·2026
Same author

Prediction of soil shear strength using hybrid machine learning approaches for performance and interpretability analysis.

Scientific reports·2026
Same author

XAI-MedNet: A Next-Generation Explainable AI Framework for Contrast-Enhanced Skin Lesion Classification via Entropy-Controlled Optimization.

Bioengineering (Basel, Switzerland)·2026
Same author

An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer.

The American journal of pathology·2026
Same author

Improving Deep Learning Based Lung Nodule Classification Through Optimized Adaptive Intensity Correction.

Bioengineering (Basel, Switzerland)·2026
Same author

An attention-based deep learning model for early detection of polyphagous shot hole borer infestations in plants.

BMC plant biology·2026
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: May 24, 2025

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression
11:23

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression

Published on: October 6, 2019

10.1K

Algorithmic and mathematical modeling for synthetically controlled overlapping.

Zafar Mahmood1, Mejdl Safran2, Abdussamad3

  • 1Department of Computer Science, University of Gujrat, Gujrat, Pakistan.

Scientific Reports
|March 3, 2025
PubMed
Summary
This summary is machine-generated.

Class overlap significantly degrades classifier performance on imbalanced data more than imbalance alone. This study introduces algorithms to generate controlled overlapping data, demonstrating its impact on various classifiers across real-world datasets.

Keywords:
Class overlapping problemsImbalanced dataMulti-class imbalance issuesSynthetic overlapping problems

More Related Videos

Automated Robotic Liquid Handling Assembly of Modular DNA Devices
11:22

Automated Robotic Liquid Handling Assembly of Modular DNA Devices

Published on: December 1, 2017

12.3K
Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy
08:25

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Published on: April 27, 2021

3.5K

Related Experiment Videos

Last Updated: May 24, 2025

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression
11:23

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression

Published on: October 6, 2019

10.1K
Automated Robotic Liquid Handling Assembly of Modular DNA Devices
11:22

Automated Robotic Liquid Handling Assembly of Modular DNA Devices

Published on: December 1, 2017

12.3K
Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy
08:25

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Published on: April 27, 2021

3.5K

Area of Science:

  • Machine Learning
  • Data Science
  • Computer Science

Background:

  • Class overlap in multi-class imbalanced datasets poses a significant challenge to classifier performance.
  • Existing research acknowledges the negative impact of class overlap but lacks quantification and detailed analysis of varying overlap levels.
  • The distinction between class imbalance and class overlap effects on classifier performance requires further investigation.

Purpose of the Study:

  • To develop and implement algorithms for synthetically generating controlled overlapping samples in multi-class datasets.
  • To quantify the impact of different levels of class overlap on classifier performance.
  • To evaluate the effectiveness of state-of-the-art classifiers when dealing with increasing class overlap.

Main Methods:

  • Four novel algorithms were implemented to generate synthetic overlapping data with controlled levels.
  • Experiments utilized multi-class datasets, including 20 real-world examples, with varying degrees of class overlap.
  • State-of-the-art non-parametric classifiers, including Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Random Forest, were employed.

Main Results:

  • The study demonstrates that class overlap has a more detrimental effect on classifier performance than data imbalance alone.
  • Generated synthetic data effectively highlighted the varying impact of different overlap levels on classifier accuracy and stability.
  • Classifiers exhibited performance degradation with increasing levels of class overlap, confirming the detrimental effect.

Conclusions:

  • Class overlap is a critical factor affecting classifier performance, often more so than imbalance in multi-class problems.
  • The proposed algorithms provide a valuable tool for creating controlled overlapping datasets to study classifier behavior.
  • Understanding and mitigating class overlap is crucial for developing robust classifiers for complex, real-world imbalanced datasets.