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

356
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:
356
Aggregates Classification01:29

Aggregates Classification

411
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...
411
Classification of Systems-II01:31

Classification of Systems-II

254
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,
254
Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

908
Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...
908
Stratified Sampling Method01:16

Stratified Sampling Method

13.2K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
13.2K
Classification of Signals01:30

Classification of Signals

996
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...
996

You might also read

Related Articles

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

Sort by
Same author

Honeycomb Enhances the Egg-Laying Capacity of Laying Hens by Modulating Ovarian Function and Yolk Precursor Synthesis.

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

Is occult stress urinary incontinence truly "occult"?

Gynecology and pelvic medicine·2026
Same author

Surveillance on African Horse Sickness Virus in China's Southern Border Regions.

Transboundary and emerging diseases·2026
Same author

Hands-free phosphoproteomics workflow with a high-throughput automated system enabled by superhydrophilic nanomaterials.

Mikrochimica acta·2026
Same author

Post-Translational Modification as an Allosteric Switch in Hsp90: How Dual Phosphorylation Locks Chaperone Complexes into Hyperstabilized States.

The journal of physical chemistry letters·2026
Same author

Research of N-acetyl-L-cysteine on CD40-CD40L pathway in pulmonary fibrosis induced by silicon dioxide.

Frontiers in genetics·2026

Related Experiment Video

Updated: Oct 10, 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

7.7K

Research on expansion and classification of imbalanced data based on SMOTE algorithm.

Shujuan Wang1, Yuntao Dai1, Jihong Shen1

  • 1College of Mathematical Sciences, Harbin Engineering University, Harbin, 150001, China.

Scientific Reports
|December 16, 2021
PubMed
Summary

This study introduces an improved SMOTE algorithm using Normal distribution to address imbalanced medical big data. The enhanced method generates synthetic minority samples more effectively, improving classification accuracy in auxiliary medical diagnosis.

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.7K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

953

Related Experiment Videos

Last Updated: Oct 10, 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

7.7K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.7K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

953

Area of Science:

  • Artificial Intelligence
  • Medical Data Science

Background:

  • Medical big data classification is crucial for auxiliary diagnosis.
  • Class imbalance in medical datasets hinders standard learning algorithms.
  • Existing SMOTE algorithms face challenges with marginalization and parameter selection.

Purpose of the Study:

  • To propose an improved SMOTE algorithm for imbalanced medical data.
  • To enhance classification performance in medical auxiliary diagnosis.
  • To mitigate the marginalization and blindness issues of standard SMOTE.

Main Methods:

  • Developed a novel SMOTE algorithm incorporating Normal distribution.
  • Generated synthetic minority samples closer to the minority class center.
  • Evaluated the algorithm on multiple imbalanced medical datasets (Pima, WDBC, WPBC, Ionosphere, Breast-cancer-wisconsin).

Main Results:

  • The proposed Normal distribution-based SMOTE algorithm outperformed the original SMOTE.
  • Improved classification accuracy was observed across various medical datasets.
  • Appropriate parameter selection maintained original data distribution characteristics, optimizing classification.

Conclusions:

  • The improved SMOTE algorithm effectively addresses class imbalance in medical big data.
  • This method enhances the reliability of AI-driven auxiliary medical diagnosis.
  • The Normal distribution approach offers a more precise way to generate synthetic data, improving classification outcomes.