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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Bayesian Network Model Averaging Classifiers by Subbagging.

Shouta Sugahara¹, Itsuki Aomi², Maomi Ueno¹

¹Graduate School of Informatics and Engineering, The University of Electro-Communications, 1-5-1, Chofugaoka, Chofu-shi 182-8585, Japan.

Entropy (Basel, Switzerland)

|May 28, 2022

Summary

This summary is machine-generated.

This study introduces subbagging with the K-best method to enhance Bayesian network classification accuracy. The novel approach improves performance, especially for small datasets, by reducing errors in model averaging.

Keywords:

Bayesian networks classification model averaging structure learning

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Area of Science:

Machine Learning
Computational Statistics

Background:

Bayesian networks (BNs) are used for classification, inferring class variables from features.
Maximizing marginal likelihood (ML) for BN structures yields lower accuracy than maximizing conditional log likelihood (CLL).
ML's asymptotic consistency is beneficial for large datasets, but small sample sizes increase learning errors, degrading classification accuracy.

Purpose of the Study:

To improve Bayesian network classification accuracy, particularly for small sample sizes.
To address the limitations of model averaging in reducing posterior standard errors.
To develop a robust method combining subbagging and the K-best approach for reliable structure learning.

Main Methods:

Implemented subbagging (a modified bagging technique using random sampling without replacement) to reduce posterior standard errors in model averaging.
Utilized the K-best method with the ML score to ensure asymptotic consistency.
Evaluated the proposed method against existing Bayesian network classification (BNC) and ensemble techniques.

Main Results:

The proposed subbagging method significantly reduces the posterior standard error of each structure in model averaging, especially with small sample sizes.
The combination of subbagging and the K-best method with ML score enhances classification accuracy compared to traditional methods.
Experimental results confirm superior performance over earlier BNC methods and other state-of-the-art ensemble approaches.

Conclusions:

The proposed subbagging approach effectively improves Bayesian network classification accuracy by mitigating errors associated with small sample sizes.
This method offers a robust solution for scenarios where traditional model averaging and ML-based structure learning falter.
The study demonstrates the potential of ensemble techniques like subbagging in advancing the reliability of Bayesian network classifiers.