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Related Concept Videos

Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first column of the Routh...
Types of Selection01:46

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
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,
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...

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Related Experiment Video

Updated: May 27, 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

Selective voting in convex-hull ensembles improves classification accuracy.

Ralph L Kodell1, Chuanlei Zhang, Eric R Siegel

  • 1Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States. rlkodell@uams.edu

Artificial Intelligence in Medicine
|November 9, 2011
PubMed
Summary

A new selective-voting algorithm enhances patient classification accuracy by accounting for population heterogeneity, improving personalized medicine. This method statistically increases predictive performance over standard approaches.

Related Experiment Videos

Last Updated: May 27, 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:

  • Computational biology
  • Bioinformatics
  • Machine learning in healthcare

Background:

  • Classification algorithms predict patient risks and responses using high-dimensional genomic data.
  • Current algorithms assume population homogeneity, limiting clinical predictive accuracy.
  • Tailoring therapies requires improved individualized treatment predictions.

Purpose of the Study:

  • To develop a novel algorithm addressing population heterogeneity for enhanced classification accuracy.
  • To improve the prediction of patient risks and responses for personalized medicine.
  • To advance the clinical utility of genomic data in disease treatment.

Main Methods:

  • A selective-voting algorithm was developed within a classifier ensemble framework.
  • The ensemble utilizes two-dimensional convex hulls of training samples.
  • Classifiers vote on test samples only if samples are within pruned convex hulls.

Main Results:

  • The selective-voting algorithm demonstrated statistically significant accuracy increases on two cancer datasets.
  • Accuracy improved from 86.0% to 89.8% (p<0.001) and 63.2% to 67.8% (p<0.003).
  • These gains were observed compared to the original algorithm without selective voting.

Conclusions:

  • Selective voting in convex-hull classifier ensembles significantly boosts classification accuracy.
  • This approach outperforms traditional one-size-fits-all classification methods.
  • The findings support the potential for more accurate, individualized patient therapy selection.