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

Per-Unit Sequence Models01:26

Per-Unit Sequence Models

361
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
361
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

325
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...
325
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

4.5K
Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
4.5K
Aggregates Classification01:29

Aggregates Classification

884
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...
884
Associative Learning01:27

Associative Learning

1.1K
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...
1.1K
Survival Tree01:19

Survival Tree

325
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...
325

You might also read

Related Articles

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

Sort by
Same author

Disentangled Multimodal Spatiotemporal Learning for Hybrid EEG-fNIRS Brain-Computer Interface.

IEEE transactions on bio-medical engineering·2026
Same author

Context-CAM: Context-Level Weight-Based CAM With Sequential Denoising to Generate High-Quality Class Activation Maps.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

Broad Multitask Learning System With Group Sparse Regularization.

IEEE transactions on neural networks and learning systems·2024
Same author

Federated learning using model projection for multi-center disease diagnosis with non-IID data.

Neural networks : the official journal of the International Neural Network Society·2024
Same author

Multi-Scale Masked Autoencoders for Cross-Session Emotion Recognition.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2024
Same author

Fast Broad Multiview Multi-Instance Multilabel Learning (FBM3L) With Viewwise Intercorrelation.

IEEE transactions on neural networks and learning systems·2023
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

IGFD-Net: Illumination-guided frequency decoupling for polarization image fusion.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Aggregating global-scale pixel-wise forgery cues within a graph.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Related Experiment Video

Updated: Dec 20, 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

Accurate and efficient sequential ensemble learning for highly imbalanced multi-class data.

Chi-Man Vong1, Jie Du2

  • 1Department of Computer and Information Science, University of Macau, Macau- SAR 999078, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 27, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel sequential ensemble learning (SEL) framework to improve accuracy and efficiency for imbalanced multi-class classification tasks. The new method significantly enhances performance on highly imbalanced datasets.

Keywords:
Highly imbalanced dataMulti-class classificationSequential ensemble learning

Related Experiment Videos

Last Updated: Dec 20, 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

Area of Science:

  • Machine Learning
  • Data Science
  • Artificial Intelligence

Background:

  • Multi-class classification with highly imbalanced data presents significant challenges in accuracy, training efficiency, and sensitivity to imbalance ratios.
  • Existing methods like AdaBoost struggle with large datasets and high imbalance ratios (IR).

Purpose of the Study:

  • To develop a novel sequential ensemble learning (SEL) framework to address the challenges of multi-class classification on highly imbalanced data.
  • To improve classification accuracy, training efficiency, and sensitivity to high imbalance ratios.

Main Methods:

  • A novel sequential ensemble learning (SEL) framework is proposed.
  • A learning strategy called balanced and majority-disjoint subsets division (BMSD) is developed to ensure class balance and subset properties.
  • A learner combination method (LCM) specifically designed for extreme learning machines (LCM-ELM) is introduced.

Main Results:

  • The proposed SEL framework with BMSD and LCM-ELM demonstrated improved performance across multiple metrics (G-mean, macro-F, micro-F, MAUC) on 16 benchmark datasets.
  • The framework effectively handles highly imbalanced multi-class data with imbalance ratios up to 14K and data sizes up to 493K.
  • Experimental results show a significant reduction in training time compared to state-of-the-art methods.

Conclusions:

  • The proposed SEL framework offers a robust solution for multi-class classification on highly imbalanced datasets.
  • The BMSD strategy and LCM-ELM effectively enhance classification performance and training efficiency.
  • This approach provides a significant advancement over traditional methods for imbalanced learning problems.