Jove
Visualize
Contact Us

Related Concept Videos

Classification of Systems-I01:26

Classification of Systems-I

341
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:
341
Classification of Systems-II01:31

Classification of Systems-II

248
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,
248
Aggregates Classification01:29

Aggregates Classification

398
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...
398
Force Classification01:22

Force Classification

1.8K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.8K
Classification of Signals01:30

Classification of Signals

965
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...
965
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

36.4K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
36.4K

You might also read

Related Articles

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

Sort by
Same author

Boxing Punch Detection with Single Static Camera.

Entropy (Basel, Switzerland)·2024
Same author

Processing Real-Life Recordings of Facial Expressions of Polish Sign Language Using Action Units.

Entropy (Basel, Switzerland)·2023
Same author

Real-World Data Difficulty Estimation with the Use of Entropy.

Entropy (Basel, Switzerland)·2021
Same author

Minimum Query Set for Decision Tree Construction.

Entropy (Basel, Switzerland)·2021
Same author

Permutation Entropy as a Measure of Information Gain/Loss in the Different Symbolic Descriptions of Financial Data.

Entropy (Basel, Switzerland)·2020
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles
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 Experiment Video

Updated: Sep 26, 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

Preference-Driven Classification Measure.

Jan Kozak1, Barbara Probierz1, Krzysztof Kania2

  • 1Department of Machine Learning, University of Economics in Katowice, 1 Maja 50, 40-287 Katowice, Poland.

Entropy (Basel, Switzerland)
|April 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new preference-driven measure for evaluating machine learning classification quality, adaptable to multi-class problems and user-defined class importance. This method offers a more nuanced assessment than traditional measures.

Keywords:
classification measuremachine learningpreference-driven classificationquality measurequality of classification

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

6.5K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K

Related Experiment Videos

Last Updated: Sep 26, 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
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

6.5K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K

Area of Science:

  • Machine Learning
  • Data Science
  • Artificial Intelligence

Background:

  • Assessing machine learning classification quality is complex, with existing measures often limited to binary classification.
  • Multi-class classification presents challenges, necessitating adaptable evaluation metrics.
  • Current methods may oversimplify quality assessment for datasets with numerous decision classes.

Purpose of the Study:

  • To propose a novel preference-driven measure (p-d) for classifier quality assessment applicable to any number of classes.
  • To enable the incorporation of relative class importance into the evaluation process.
  • To demonstrate a flexible approach for adapting classifier assessment to specific analytical needs via preference vectors.

Main Methods:

  • Development of a new preference-driven measure (p-d) for classification quality.
  • Application of the p-d measure to a two-class decision problem for visualization.
  • Testing the p-d measure on real-world multi-class datasets.
  • Demonstration of user-specific preference adjustment for classifier assessment.

Main Results:

  • The preference-driven measure effectively evaluates classifier quality across multi-class scenarios.
  • The p-d measure allows for tailored assessments based on user-defined class priorities.
  • Comparative analysis shows the p-d measure can identify superior classifiers based on specific preferences, outperforming classical metrics.

Conclusions:

  • The proposed preference-driven measure offers a robust and adaptable solution for evaluating multi-class classification.
  • This approach enhances classifier selection by aligning evaluation with specific problem contexts and user priorities.
  • The p-d measure provides a more insightful alternative to traditional classification quality assessment methods.