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

Entropy01:18

Entropy

The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
Entropy02:39

Entropy

Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
Classification of Systems-I01:26

Classification of Systems-I

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:
Classification of Systems-II01:31

Classification of Systems-II

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,
Classification of Signals01:30

Classification of Signals

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...
Frequency-dependent Selection01:21

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.Positive Frequency-Dependent SelectionIn positive...

You might also read

Related Articles

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

Sort by
Same author

Developmental Changes in Pharyngeal Airway in the Male Population From Adolescence to Adulthood.

European journal of paediatric dentistry·2024
Same author

X-linked adrenoleukodystrophy in a child.

QJM : monthly journal of the Association of Physicians·2024
Same author

[Cytopathological features of hyalinizing trabecular tumor of the thyroid].

Zhonghua bing li xue za zhi = Chinese journal of pathology·2022
Same author

[Construction of prediction model combined dual-energy CT quantitative parameters and conventional CT features for assessing the Ki-67 expression levels in invasive breast cancer].

Zhonghua yi xue za zhi·2022
Same author

Efficient solid-state Raman yellow laser at 579.5  nm.

Optics letters·2020
Same author

Roles of nurses and National Nurses Associations in combating COVID-19: Taiwan experience.

International nursing review·2020
Same journal

Strategic Ability Updating in Concurrent Games by Coalitional Commitment.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2015
Same journal

Meta-Analysis of the First Facial Expression Recognition Challenge.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Adjustable model-based fusion method for multispectral and panchromatic images.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Face Feature Weighted Fusion Based on Fuzzy Membership Degree for Video Face Recognition.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

A New Adaptive Fast Cellular Automaton Neighborhood Detection and Rule Identification Algorithm.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Human-arm-and-hand-dynamic model with variability analyses for a stylus-based haptic interface.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
See all related articles

Related Experiment Video

Updated: Jul 7, 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

An efficient fuzzy classifier with feature selection based on fuzzy entropy.

H M Lee1, C M Chen, J M Chen

  • 1Dept. of Electron. Eng., Nat. Taiwan Univ. of Sci. & Technol., Taipei.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 5, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient fuzzy classifier that uses fuzzy entropy for feature selection and pattern classification. This method reduces computational load, leading to faster training and classification times with high accuracy.

Related Experiment Videos

Last Updated: Jul 7, 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

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Pattern classification often faces challenges with high dimensionality and redundant features.
  • Existing fuzzy classifiers can be computationally intensive, leading to long training and classification times.

Purpose of the Study:

  • To propose an efficient fuzzy classifier with integrated feature selection capabilities.
  • To leverage fuzzy entropy for evaluating pattern distribution and optimizing decision regions.
  • To reduce classifier complexity and improve computational efficiency.

Main Methods:

  • Developed a fuzzy classifier utilizing a novel fuzzy entropy measure.
  • Employed fuzzy entropy to assess pattern distribution and partition the pattern space into non-overlapping decision regions.
  • Integrated a feature selection procedure based on fuzzy entropy to discard irrelevant and redundant features.

Main Results:

  • The proposed fuzzy classifier demonstrated significantly reduced complexity and computational load.
  • Achieved extremely short training and classification times due to non-overlapping decision regions.
  • The feature selection process effectively reduced dimensionality and removed noisy features.
  • Validated the classifier's performance on the Iris and Wisconsin breast cancer datasets, showing promising results.

Conclusions:

  • The proposed fuzzy entropy-based classifier is efficient and effective for pattern classification tasks.
  • The method offers a robust approach to feature selection, enhancing classifier performance.
  • The classifier achieves high accuracy with reduced computational requirements.