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Updated: Jun 13, 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

Feature selection for genomic data sets through feature clustering.

Fengbin Zheng¹, Xiajiong Shen, Zhengye Fu

¹College of Computer and Information Engineering, Henan University, Kaifeng, Henan 475004, China. zhengfb@henu.edu.cn

International Journal of Data Mining and Bioinformatics

|April 29, 2010

Summary

This summary is machine-generated.

Feature selection using clustering (FSFC) works for both supervised and unsupervised learning. This novel algorithm reduces data size without impacting clustering or classification quality.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Area of Science:

Bioinformatics
Machine Learning
Data Science

Background:

Supervised feature selection may not perform well in unsupervised learning tasks, and vice versa.
Existing methods often require class labels, limiting their applicability.

Purpose of the Study:

To introduce a novel feature selection algorithm, FSFC (Feature Selection through Feature Clustering).
To develop a method suitable for both supervised and unsupervised learning without needing class labels.
To evaluate FSFC's effectiveness in reducing dataset size while preserving data quality.

Main Methods:

FSFC algorithm utilizes feature clustering to identify relevant features.
The algorithm operates without requiring class label information from the dataset.
FSFC was tested on biological datasets for both clustering and classification.

Main Results:

FSFC effectively reduces the dimensionality of biological datasets.
The algorithm maintains the quality of clustering results after feature reduction.
Classification performance remains high even after significant data reduction using FSFC.

Conclusions:

FSFC is a versatile feature selection method applicable to both supervised and unsupervised learning.
The algorithm offers significant data reduction capabilities for biological data analysis.
FSFC provides a valuable tool for improving the efficiency of machine learning tasks in bioinformatics.