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Related Concept Videos

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...
Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Multiple Allele Traits01:49

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Multiple Regression

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Related Experiment Video

Updated: Jun 2, 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

Methods of forward feature selection based on the aggregation of classifiers generated by single attribute.

Linkai Luo1, Lingjun Ye, Meixiang Luo

  • 1Department of Automation, Xiamen University, Xiamen 361005, PR China. luolk@xmu.edu.cn

Computers in Biology and Medicine
|May 10, 2011
PubMed
Summary

New forward feature selection (FFS) methods improve gene selection efficiency. FFS-ACSA2 demonstrates superior performance, rivaling established techniques like support vector machine-based recursive feature elimination (SVM-RFE).

Related Experiment Videos

Last Updated: Jun 2, 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:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Forward feature selection (FFS) offers faster convergence than backward feature selection (BFS) for gene expression data.
  • Existing FFS methods require enhancement for greater efficiency and effectiveness in gene selection.

Purpose of the Study:

  • To develop novel, efficient Forward Feature Selection (FFS) methods for gene selection.
  • To introduce a new p-insensitive loss function to improve classifier ensemble pruning.

Main Methods:

  • Proposed two FFS methods (FFS-ACSA1 and FFS-ACSA2) utilizing pruned classifier ensembles.
  • Introduced a novel p-insensitive loss function to address limitations of the margin Euclidean distance loss function.
  • Evaluated methods on four gene expression datasets.

Main Results:

  • FFS-ACSA2 achieved the best performance among tested FFS methods, including signal-to-noise ratio (SNR) and FFS-ACSA1.
  • FFS-ACSA2 demonstrated competitive results compared to support vector machine-based recursive feature elimination (SVM-RFE).
  • FFS-ACSA1 exhibited instability in performance.

Conclusions:

  • FFS-ACSA2 is a highly effective and efficient gene selection method.
  • The proposed p-insensitive loss function enhances classifier ensemble pruning for FFS.
  • FFS-ACSA2 offers a promising alternative to existing gene selection techniques.