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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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TAN-FGBMLE: Tree-Augmented Naive Bayes Structure Learning Based on Fast Generative Bootstrap Maximum Likelihood
Chenghao Wei1,2, Tianyu Zhang1,2, Chen Li1,2
1School of Computer Science, Hubei University of Technology, Wuhan 430068, China.
Entropy (Basel, Switzerland)
|December 24, 2025
Summary
We developed a new method for Tree-Augmented Naive Bayes (TAN) structure learning using Fast Generative Bootstrap Maximum Likelihood Estimation (TAN-FGBMLE). This approach improves density estimation for continuous attributes, enhancing model accuracy and interpretability.
Area of Science:
- Machine Learning
- Statistical Modeling
- Data Mining
Background:
- Tree-Augmented Naive Bayes (TAN) offers interpretable graphical models.
- TAN's structure learning for continuous data relies on class-conditional mutual information.
- Estimating density for complex distributions in TAN is challenging.
Purpose of the Study:
- To propose a novel structure learning method for TAN.
- To address limitations in density estimation for continuous attributes.
- To enhance the accuracy and efficiency of TAN models.
Main Methods:
- Introduced Fast Generative Bootstrap Maximum Likelihood Estimation (TAN-FGBMLE).
- Employed a two-stage FGBMLE process for rapid parameter generation and optimal weight estimation.
- Utilized Prim's algorithm for TAN structure construction.
Main Results:
- TAN-FGBMLE demonstrated superior fitting accuracy and reduced runtime compared to traditional estimators.
- Achieved higher accuracy and recall on open-source datasets, showing robustness and interpretability.
- Applied to air quality data, it yielded high classification results and captured attribute dependencies effectively.
Conclusions:
- TAN-FGBMLE provides a robust and efficient solution for TAN structure learning with continuous attributes.
- The method enhances density estimation, leading to improved model performance.
- It offers a valuable tool for analyzing complex datasets and uncovering attribute relationships.
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