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Related Experiment Video

Updated: Mar 8, 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

One-Class Classifiers Based on Entropic Spanning Graphs.

Lorenzo Livi, Cesare Alippi

IEEE Transactions on Neural Networks and Learning Systems

|January 24, 2017

Summary

This summary is machine-generated.

This study introduces a novel one-class classifier design using entropic spanning graphs for outlier detection. The method effectively handles non-numeric data and complex structures, demonstrating versatility in experiments.

Related Experiment Videos

Last Updated: Mar 8, 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:

Machine Learning
Data Science
Graph Theory

Background:

One-class classifiers are crucial for identifying outliers in datasets.
Existing methods may struggle with non-numeric data or complex data structures.

Purpose of the Study:

To propose a new design methodology for one-class classifiers.
To enable processing of non-numeric data and handle complex geometric structures.
To provide a confidence level for classifier decisions.

Main Methods:

Utilizing entropic spanning graphs for classifier design.
Employing an embedding procedure for non-numeric data.
Applying mutual information minimization and a graph-based fuzzy model for confidence estimation.

Main Results:

The proposed method effectively classifies outliers using entropic spanning graphs.
Demonstrated success on benchmarks with feature vectors and labeled graphs.
Achieved effective protein solubility recognition using various data representations.

Conclusions:

The entropic spanning graph-based one-class classifier is effective and versatile.
The method successfully handles non-numeric data and complex data geometries.
Experimental results show superior performance compared to state-of-the-art approaches.