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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Parameter selection of Gaussian kernel for one-class SVM.

Yingchao Xiao, Huangang Wang, Wenli Xu

IEEE Transactions on Cybernetics

|August 8, 2014

Summary

This summary is machine-generated.

This study introduces a new method for selecting parameters in one-class classification (OCC) using one-class SVM (OCSVM). The approach optimizes parameter selection for improved fault detection and other OCC applications.

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Area of Science:

Machine Learning
Artificial Intelligence
Pattern Recognition

Background:

One-class classification (OCC) models target class data only.
OCC is crucial for applications like fault detection.
One-class SVM (OCSVM) with Gaussian kernel is effective but parameter selection is challenging.

Purpose of the Study:

To propose a novel method for optimal kernel parameter selection in OCSVM.
To address the open problem of parameter selection affecting OCSVM performance.

Main Methods:

Developed a method to measure sample distances to OCSVM enclosing surfaces.
Formulated an optimization objective function for parameter selection based on these measurements.

Main Results:

Extensive experiments were conducted on diverse datasets.
The proposed method demonstrated effectiveness in parameter selection for OCSVM.

Conclusions:

The novel parameter selection method enhances OCSVM performance.
This addresses a critical challenge in one-class classification applications.