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

How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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...
Aggregates Classification01:29

Aggregates Classification

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...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
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...

You might also read

Related Articles

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

Sort by
Same author

Reconstruction of MRI from undersampled k-spaces of double-contrast volume acquisitions using deep neural networks.

Magnetic resonance imaging·2026
Same author

Sex-related structural alterations across common epilepsies: a worldwide ENIGMA study.

bioRxiv : the preprint server for biology·2026
Same author

Editorial: Cognitive enhancement by brain stimulation techniques.

Frontiers in human neuroscience·2026
Same author

Abnormal functional connectivity patterns in temporal lobe epilepsy-An international ENIGMA-epilepsy study.

Epilepsia open·2026
Same author

Altered resting-state functional connectivity in delusional patients with schizophrenia or schizoaffective disorder: An fMRI study using threshold-free cluster-enhancement.

Psychiatry research. Neuroimaging·2026
Same author

fMRI features in recent suicide attempters performing the future imagination task.

Psychiatry research. Neuroimaging·2025

Related Experiment Video

Updated: Jun 15, 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

Selection-Fusion Approach for Classification of Datasets with Missing Values.

Mostafa Ghannad-Rezaie1, Hamid Soltanian-Zadeh, Hao Ying

  • 1Department of Diagnostic Radiology, Henry Ford Hospital, Detroit, MI 48202, USA.

Pattern Recognition
|March 10, 2010
PubMed
Summary

This study introduces a novel method for classifying incomplete data by discovering missing value patterns. It effectively handles datasets with few samples and many missing values, outperforming existing techniques.

Related Experiment Videos

Last Updated: Jun 15, 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:

  • Data Science
  • Machine Learning
  • Bioinformatics

Background:

  • Classification of incomplete datasets presents challenges, especially with limited samples and high missing data percentages.
  • Existing missing value treatment methods often struggle with such data characteristics.

Purpose of the Study:

  • To propose a new approach for classifying incomplete data by leveraging missing value patterns.
  • To develop a method that performs well on datasets with small sample sizes and high proportions of missing values.

Main Methods:

  • The approach identifies subsets of samples with minimal missing features based on missing value patterns.
  • Classifiers are trained on these subsets, and their outputs are combined.
  • Subset selection is framed as a clustering problem with a derived mathematical framework.
  • A numerical criterion is proposed to balance computational complexity and classification accuracy.

Main Results:

  • The proposed method was evaluated on eight diverse datasets, including UCI data mining archive and an epilepsy dataset.
  • Experimental results demonstrate superior classification accuracy compared to multiple imputation and four other methods.
  • The degree of accuracy improvement is contingent upon the specific missing value patterns and percentages within the data.

Conclusions:

  • The novel missing value pattern discovery approach offers a robust solution for classifying incomplete data, particularly in challenging scenarios.
  • The method's effectiveness is validated across multiple datasets, showing significant improvements over established techniques.
  • Future work could explore further optimization of the trade-off between computational cost and predictive performance.