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Exact Learning Augmented Naive Bayes Classifier.

Shouta Sugahara¹, Maomi Ueno¹

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

Entropy (Basel, Switzerland)

|December 24, 2021

Summary

This summary is machine-generated.

This study compares Bayesian network (BN) learning algorithms. Exact learning with marginal likelihood (ML) outperforms conditional log likelihood (CLL) on large datasets, but an augmented naive Bayes classifier (ANB) is proposed for small datasets.

Keywords:

Bayesian networks augmented naive Bayes classifier classification structure learning

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Area of Science:

Machine Learning
Artificial Intelligence
Computer Science

Background:

Bayesian networks (BNs) are widely used for classification.
Previous studies suggested conditional log likelihood (CLL) outperformed marginal likelihood (ML) for BN classification.
This performance difference was potentially due to the use of approximate learning algorithms.

Purpose of the Study:

To compare the classification accuracies of BNs using approximate CLL learning versus exact ML learning.
To address limitations of exact ML learning in BNs with small sample sizes and complex class variable dependencies.
To propose an improved classifier for enhanced performance under challenging data conditions.

Main Methods:

Comparison of classification accuracies between approximate CLL and exact ML learning algorithms for Bayesian networks.
Investigation of BN performance under varying sample sizes and class variable parent counts.
Development and evaluation of an exact learning augmented naive Bayes classifier (ANB).

Main Results:

Exact ML learning for BNs achieved higher classification accuracy than approximate CLL learning on large datasets.
Exact ML learning BNs performed poorly with small sample sizes and numerous class variable parents.
The proposed augmented naive Bayes classifier (ANB) demonstrated superior performance in comparative experiments.

Conclusions:

The choice of learning algorithm and scoring function significantly impacts BN classification accuracy.
Exact ML learning is effective for large datasets but requires modification for small, complex datasets.
The augmented naive Bayes classifier (ANB) offers a robust solution for accurate classification in challenging scenarios.