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Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data

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Local-learning-based feature selection for high-dimensional data analysis.

Yijun Sun¹, Sinisa Todorovic, Steve Goodison

¹Interdisciplinary Center for Biotechnology Research, University of Florida, PO Box 103622, Gainesville, FL 32610-3622, USA. sunyijun@biotech.ufl.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence

|July 17, 2010

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This study introduces an efficient feature selection algorithm for data classification. It effectively handles numerous irrelevant features, improving accuracy and speed for machine learning tasks.

Area of Science:

Machine Learning
Data Science
Computer Science

Background:

High-dimensional data presents challenges in feature selection due to numerous irrelevant features.
Existing feature selection methods often suffer from implementation difficulties, high computational costs, and suboptimal accuracy.

Purpose of the Study:

To propose a novel feature selection algorithm designed for data classification with a large number of irrelevant features.
To address limitations of prior work concerning algorithm implementation, computational complexity, and solution accuracy.

Main Methods:

The algorithm decomposes complex nonlinear problems into locally linear ones using local learning.
Feature relevance is learned globally within a large margin framework.
It leverages established machine learning and numerical analysis techniques without data distribution assumptions.

Related Experiment Videos

Last Updated: Jun 10, 2026

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data

Published on: May 16, 2022

Main Results:

The algorithm processes thousands of features rapidly on a personal computer, achieving high accuracy.
Accuracy remains robust despite an increasing number of irrelevant features.
Theoretical analysis indicates logarithmic sample complexity concerning the number of features.

Conclusions:

The proposed algorithm offers a viable and effective solution for feature selection in supervised learning.
It demonstrates superior performance and efficiency compared to existing methods in handling high-dimensional data.