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

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

596
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
596
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.4K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
4.4K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

7.0K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
7.0K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.9K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
8.9K
Weighted Mean00:57

Weighted Mean

7.5K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
7.5K
Classification of Systems-II01:31

Classification of Systems-II

572
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,
572

You might also read

Related Articles

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

Sort by
Same author

Machine Learning Aided Kinematic Profiling of Reaching Movements Separates Spinocerebellar Ataxia type 12 and Essential Tremor.

Cerebellum (London, England)·2026
Same author

Leveraging Feature Alignment in Grassmannian Manifold for Multi-Output Regression Tasks.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Light regulation of seed-to-seedling transition under dynamic environment.

Plant physiology·2026
Same author

Chalcogenated biazulenediimides: A promising class of triplet harvesters.

The Journal of chemical physics·2025
Same author

Generative adversarial network augmented data for improved heart sound abnormality detection.

Computers in biology and medicine·2025
Same author

Can Graph Neural Networks Tackle Heterophily? Yes, With a Label-Guided Graph Rewiring Approach!

IEEE transactions on neural networks and learning systems·2025

Related Experiment Video

Updated: Apr 6, 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

Near-Bayesian Support Vector Machines for imbalanced data classification with equal or unequal misclassification

Shounak Datta1, Swagatam Das1

  • 1Electronics and Communication Sciences Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata-700 108, India.

Neural Networks : the Official Journal of the International Neural Network Society
|July 27, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a Near-Bayesian Support Vector Machine (NBSVM) to address challenges in imbalanced classification. The NBSVM method improves performance on datasets with under-represented classes by adjusting decision boundaries and regularization costs.

Keywords:
Bayes errorDecision boundary shiftImbalanced dataMulti-class classificationSupport Vector MachinesUnequal costs

More Related Videos

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.9K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.8K

Related Experiment Videos

Last Updated: Apr 6, 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
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.9K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.8K

Area of Science:

  • Machine Learning
  • Data Science
  • Artificial Intelligence

Background:

  • Standard Support Vector Machines (SVMs) often underperform with imbalanced datasets where target classes are under-represented.
  • Data imbalance poses a significant challenge in various classification tasks, impacting model accuracy and reliability.

Purpose of the Study:

  • To propose a novel approach, the Near-Bayesian Support Vector Machine (NBSVM), specifically designed to handle imbalanced classification problems.
  • To enhance the performance of SVMs on datasets with significant class disparities.

Main Methods:

  • The proposed Near-Bayesian Support Vector Machine (NBSVM) combines decision boundary shifting with unequal regularization costs.
  • The method utilizes class representation fractions to dynamically adjust the boundary and costs, extending to multi-class scenarios and unequal misclassification costs.
  • A modified Sequential Minimal Optimization (SMO) algorithm is presented for efficient NBSVM optimization.

Main Results:

  • The NBSVM approach demonstrates competitive performance on imbalanced datasets when compared to standard SVM and other state-of-the-art methods.
  • The method effectively addresses the poor performance of traditional SVMs in scenarios with under-represented classes.

Conclusions:

  • The Near-Bayesian Support Vector Machine (NBSVM) offers a robust and effective solution for imbalanced classification tasks.
  • The proposed method provides a significant improvement over standard SVMs, particularly in real-world datasets with skewed class distributions.