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Related Concept Videos

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...
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Related Experiment Video

Updated: Jul 7, 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

Switching between selection and fusion in combining classifiers: an experiment.

L I Kuncheva1

  • 1Sch. of Informatics, Univ. of Wales, Bangor.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 2, 2008
PubMed
Summary

This study introduces a novel classifier combination strategy (CS+DT) that intelligently switches between classifier selection and fusion. This approach enhances performance by leveraging classifier strengths in specific feature spaces, outperforming individual methods and simpler combinations.

Related Experiment Videos

Last Updated: Jul 7, 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:

  • Machine Learning
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Classifier combination techniques are crucial for improving model performance and robustness.
  • Existing methods like dynamic classifier selection (DCS) and simple fusion rules have limitations.
  • Intelligent switching between selection and fusion based on feature space characteristics is underexplored.

Purpose of the Study:

  • To propose and evaluate a novel hybrid classifier combination scheme, termed clustering-and-selection plus decision templates (CS+DT).
  • To compare the performance of CS+DT against its individual components and various established classifier combination methods.
  • To investigate the conditions under which classifier selection can be misled by ensemble diversity.

Main Methods:

  • A hybrid approach combining classifier selection (clustering-and-selection, CS) in dominant regions and classifier fusion (decision templates, DT) in other regions.
  • Experimental comparison using five datasets with both homogeneous (multilayer perceptrons) and heterogeneous classifier ensembles.
  • Evaluation against baseline methods including majority vote, naive Bayes, joint-distribution methods (BKS), DCS_LA, and simple fusion techniques (max, min, average, product).

Main Results:

  • The proposed CS+DT scheme demonstrated superior performance compared to its individual components (CS and DT) and most other evaluated methods.
  • The study identified scenarios where classifier selection, both static and dynamic, can be suboptimal due to inherent differences within the classifier ensemble.
  • The effectiveness of the CS+DT approach was validated across diverse datasets and ensemble configurations.

Conclusions:

  • The CS+DT method offers an effective strategy for combining classifiers, balancing selection and fusion for improved predictive accuracy.
  • Understanding the interplay between ensemble diversity and classifier selection mechanisms is critical for designing robust combination strategies.
  • The proposed method provides a valuable alternative to existing classifier combination techniques, particularly in complex pattern recognition tasks.