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

Multiple Bar Graph01:07

Multiple Bar Graph

10.4K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
10.4K
Classification of Systems-I01:26

Classification of Systems-I

649
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:
649
Classification of Systems-II01:31

Classification of Systems-II

544
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,
544
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

47.3K
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...
47.3K
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
Bar Graph01:07

Bar Graph

23.6K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
23.6K

You might also read

Related Articles

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

Sort by
Same author

Graph embedded rules for explainable predictions in data streams.

Neural networks : the official journal of the International Neural Network Society·2020
Same author

[Clinical application of micro transverse flap pedicled with superficial palmar branch of radial artery from palmar wrist to repair skin defect of finger].

Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery·2014
Same author

Biotransformations of racemic 2,3-allenenitriles in biphasic systems: synthesis and transformations of enantioenriched axially chiral 2,3-allenoic acids and their derivatives.

The Journal of organic chemistry·2014
Same author

The relationship between job performance and perceived organizational support in faculty members at Chinese universities: a questionnaire survey.

BMC medical education·2014
Same author

Preparation of hydrazine functionalized polymer brushes hybrid magnetic nanoparticles for highly specific enrichment of glycopeptides.

The Analyst·2014
Same author

Enantioseparation of new triadimenol antifungal active compounds by electrokinetic chromatography and molecular modeling study of chiral recognition mechanisms.

Electrophoresis·2014
Same journal

A practical design of backdoor trigger under frequency-based orthogonality constraints.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

EEG fine-grained visual semantic decoding via a multimodal framework.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Collaborative-adversarial jailbreaking: A propagation-aware attack framework for multi-agent code generation systems.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Theoretical analysis of the denoising autoencoder using Tweedie's formula.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Frequency-based cross-attention fusion network for RGB-D salient object detection.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

HTNet: A self-supervised heterogeneous triple network for multi-modal data.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Related Experiment Video

Updated: Mar 12, 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

Attribute-based Decision Graphs: A framework for multiclass data classification.

João Roberto Bertini1, Maria do Carmo Nicoletti2, Liang Zhao3

  • 1School of Technology, University of Campinas, R. Paschoal Marmo 1888, Jd. Nova Itália, Limeira, SP 13484-332, Brazil.

Neural Networks : the Official Journal of the International Neural Network Society
|November 5, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces the Attribute-based Decision Graph (AbDG), a novel graph construction method for machine learning. AbDG offers robust data classification by combining attribute and graph-based techniques, outperforming traditional algorithms.

Keywords:
Attribute-based Decision GraphsData-graph constructionGraph-based classificationMissing attribute valuesMulticlass classification

Related Experiment Videos

Last Updated: Mar 12, 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

Area of Science:

  • Computer Science
  • Machine Learning
  • Data Mining

Background:

  • Graph-based algorithms are common in machine learning, but traditional methods for building graphs from vector data have limitations.
  • Existing approaches depend on specific distance metrics and can be biased by local data information.

Purpose of the Study:

  • To propose a novel algorithm for constructing Attribute-based Decision Graphs (AbDG) for data classification.
  • To explore an alternative to traditional graph construction methods in machine learning.

Main Methods:

  • The Attribute-based Decision Graph (AbDG) algorithm partitions data attribute ranges into intervals, representing each as a vertex.
  • Edges connect vertices from different attributes based on a predefined pattern.
  • Classification is achieved by matching new instance attribute values with the AbDG structure.

Main Results:

  • AbDG demonstrates competitive performance compared to established multiclass classification algorithms.
  • The framework effectively handles missing attribute values, enhancing its practical applicability.
  • Classification can be performed using only a subset of the input data's attributes.

Conclusions:

  • Attribute-based Decision Graphs (AbDG) offer a robust approach to pattern matching and data classification.
  • AbDG successfully integrates the strengths of attribute-based and graph-based methodologies.
  • The method provides flexibility in data analysis by allowing the use of partial attribute information.