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Related Experiment Video

Updated: Jul 7, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Fast orthogonal forward selection algorithm for feature subset selection.

¹Sch. of Electr. and Electron. Eng., Nanyang Technol. Univ., Singapore.

IEEE Transactions on Neural Networks

|February 5, 2008

Summary

We introduce a fast orthogonal forward selection (FOFS) algorithm for efficient feature subset selection in pattern classification. This method reduces computational cost compared to traditional orthogonal forward selection (OFS).

Related Experiment Videos

Last Updated: Jul 7, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Computer Science
Machine Learning
Pattern Recognition

Background:

Feature selection is crucial for optimizing pattern classification models.
High dimensionality and feature correlations pose computational challenges.

Purpose of the Study:

To develop a computationally efficient algorithm for feature subset selection.
To introduce the Fast Orthogonal Forward Selection (FOFS) algorithm.

Main Methods:

The FOFS algorithm utilizes an implicit orthogonal transform to handle feature correlations.
It decomposes correlations among candidate features without explicit computation.

Main Results:

FOFS achieves significant reductions in computational effort compared to conventional Orthogonal Forward Selection (OFS).
The implicit decomposition enhances processing speed for feature selection.

Conclusions:

The FOFS algorithm offers a faster and more efficient approach to feature subset selection.
This method is beneficial for large-scale pattern classification tasks.