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

647
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:
647
Classification of Systems-II01:31

Classification of Systems-II

540
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,
540
Survival Tree01:19

Survival Tree

453
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...
453
Classification of Signals01:30

Classification of Signals

1.5K
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...
1.5K
Aggregates Classification01:29

Aggregates Classification

1.1K
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...
1.1K
Classification of Leukocytes01:30

Classification of Leukocytes

6.8K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
6.8K

You might also read

Related Articles

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

Sort by
Same author

Advances in Activity/Property Prediction from Chemical Structures.

Critical reviews in analytical chemistry·2022
Same author

A quantitative reliability metric for querying large database.

Forensic science international·2021
Same author

In Situ Determination of Cannabidiol in Hemp Oil by Near-Infrared Spectroscopy.

Journal of natural products·2021
Same author

Analysis of Wine and Its Use in Tracing the Origin of Grape Cultivation.

Critical reviews in analytical chemistry·2021
Same author

Self-Optimizing Support Vector Elastic Net.

Analytical chemistry·2020
Same author

Pipeline for High-Throughput Modeling of Marijuana and Hemp Extracts.

Analytical chemistry·2019

Related Experiment Video

Updated: Mar 8, 2026

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

1.7K

Support vector machine classification trees based on fuzzy entropy of classification.

Peter de Boves Harrington1

  • 1Ohio University, Center for Intelligent Chemical Instrumentation, Department of Chemistry and Biochemistry, Clippinger Laboratories, Athens, OH 45701-2979, USA.

Analytica Chimica Acta
|January 14, 2017
PubMed
Summary
This summary is machine-generated.

A new fuzzy entropy algorithm effectively partitions complex, overlapped data for improved classification. This method enhances support vector machine (SVM) tree classifiers, outperforming traditional gap-finding techniques on large datasets.

Keywords:
AuthenticationChemometricsFuzzy entropyKernel entropy of classificationSVM classification treeSVMTreeGSVMTreeHTea

More Related Videos

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.1K

Related Experiment Videos

Last Updated: Mar 8, 2026

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

1.7K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.1K

Area of Science:

  • Machine Learning
  • Data Mining
  • Computational Chemistry

Background:

  • Support Vector Machines (SVM) are powerful classifiers, but their application in classification trees (SVMTreeG) struggles with large, complex datasets lacking clear data gaps.
  • Existing methods for data partitioning may fail when data clusters overlap, limiting classifier performance.

Purpose of the Study:

  • To develop a novel algorithm using fuzzy entropy for optimal data partitioning in overlapped datasets.
  • To create a kernelized version of the fuzzy entropy algorithm.
  • To evaluate the performance of the new algorithm against existing methods using diverse datasets.

Main Methods:

  • A novel fuzzy entropy algorithm was devised to identify optimal data partitions, particularly for overlapped data clusters.
  • A kernelized version of the fuzzy entropy algorithm was developed.
  • A fast support vector machine (SVM) implementation, optimized for speed by omitting cost C and slack variables, was utilized.
  • Statistical comparisons were performed using bootstrapped Latin partitions on synthetic XOR data and real-world NMR spectra from tea extracts.

Main Results:

  • The fuzzy entropy algorithm successfully identified optimal partitions in overlapped data spaces, overcoming limitations of gap-finding classifiers.
  • The novel algorithm demonstrated robust performance in statistical comparisons against other tree classifiers.
  • Validation on large prediction sets (50,000 objects) and NMR spectral data confirmed the algorithm's effectiveness.

Conclusions:

  • The fuzzy entropy-based algorithm offers a significant advancement for data classification, especially in scenarios with complex and overlapped data structures.
  • This approach enhances the capabilities of support vector machine (SVM) tree classifiers, providing a more reliable method for analyzing large and intricate datasets.
  • The developed algorithm shows promise for applications in diverse fields requiring sophisticated data analysis, such as chemometrics.