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Related Concept Videos

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.
Harmonic Mean01:09

Harmonic Mean

The arithmetic mean is usually skewed towards the larger values in the data set. Therefore, to avoid this inherent bias towards smaller values, the harmonic mean is used.
Take the example of the speed of a car, which is the measure of the rate of distance traveled. If the vehicle traverses the same distance back-and-forth, its average speed equals the total distance traveled divided by the total time taken. However, if the car moves with varying speeds, then the arithmetic mean is more skewed...
Parallel Resonance01:23

Parallel Resonance

The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:
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...
Affinity and Avidity01:41

Affinity and Avidity

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Related Experiment Video

Updated: May 21, 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

Feature selection with harmony search.

Ren Diao1, Qiang Shen

  • 1Department of Computer Science, Aberystwyth University, Aberystwyth, UK. rrd09@aber.ac.uk

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|May 31, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel feature selection (FS) method using harmony search (HS). The approach enhances subset quality and reduces complexity, outperforming traditional methods like hill climbing.

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Pattern-based Search of Epigenomic Data Using GeNemo
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Published on: October 11, 2018

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06:38

Pattern-based Search of Epigenomic Data Using GeNemo

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Area of Science:

  • Machine Learning
  • Data Mining
  • Computational Intelligence

Background:

  • Feature selection (FS) is crucial for identifying optimal data subsets.
  • Existing methods like hill climbing (HC) and nature-inspired heuristics often struggle with local optima and complexity.

Purpose of the Study:

  • To present a novel feature selection (FS) approach utilizing harmony search (HS).
  • To enhance the efficiency and effectiveness of feature selection processes.

Main Methods:

  • Developed a new FS approach based on harmony search (HS).
  • Integrated HS with various subset evaluation techniques.
  • Introduced parameter control schemes and an iterative refinement strategy.

Main Results:

  • The HS-based FS approach demonstrates reduced complexity and improved subset quality.
  • The method effectively escapes local solutions, identifying multiple optimal subsets due to its stochastic nature.
  • Performance was compared favorably against hill climbing (HC), genetic algorithms, and particle swarm optimization.

Conclusions:

  • Harmony search (HS) offers a promising and efficient alternative for feature selection (FS).
  • The proposed method provides a robust framework for discovering high-quality feature subsets.
  • The approach is generalizable and adaptable to various feature selection tasks.