Artificial intelligence in gradient boosting techniques

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Artificial Intelligence and Gradient Boosting Techniques

Overview of Gradient Boosting in Artificial Intelligence

Gradient boosting is a powerful machine learning technique widely used in artificial intelligence for both regression and classification tasks. It works by building an ensemble of weak prediction models, typically decision trees, in a stage-wise manner. Each new model is trained to correct the errors made by the previous models, optimizing an arbitrary differentiable loss function. This approach has led to significant success in various AI applications due to its flexibility and strong predictive performance 36.

Popular Gradient Boosting Algorithms: XGBoost, LightGBM, and CatBoost

Several advanced gradient boosting algorithms have been developed to improve speed, accuracy, and scalability. XGBoost is known for its reliability and efficiency, making it a popular choice in machine learning competitions. LightGBM focuses on extremely fast training by using techniques like Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB), which help handle large datasets and high-dimensional features efficiently. CatBoost introduces modifications to gradient computation to avoid prediction shift, enhancing model accuracy. Comparative studies show that CatBoost generally achieves the best accuracy, LightGBM is the fastest, and XGBoost balances both speed and accuracy well 289.

Integration with Neural Networks and Hybrid Models

Recent research has explored integrating gradient boosting with neural networks. One approach involves sequentially training the final layers of a neural network using gradient boosting, which allows for competitive generalization performance compared to traditional neural network training methods. This method also enables dynamic adaptation of the model for different speed requirements during inference, offering flexibility in real-world AI applications .

Hybrid models that combine gradient boosting with other machine learning techniques, such as natural gradient boosting or deep learning, have demonstrated improved predictive accuracy and uncertainty estimation. These hybrid approaches are particularly effective in complex prediction tasks, such as construction cost estimation and structural engineering applications 49.

Explainability and Feature Contribution in Gradient Boosting

Despite their strong performance, gradient boosting models are often considered "black-box" due to their complexity. To address this, explainable artificial intelligence (XAI) methods have been developed to interpret both global and local model behavior. Techniques such as feature contribution analysis and explainable machine learning visualizations help identify which features are most influential in the model's predictions. These methods not only improve transparency but also support compliance with regulations like GDPR and enhance user trust in AI systems 510.

Applications in Engineering and Drug Development

Gradient boosting techniques have been successfully applied in various engineering fields, such as predicting the strength of concrete-filled steel tubular columns and the adhesion strength of micropatterned surfaces. In drug development, XGBoost has been used for classification and regression tasks, providing practical solutions for analyzing clinical trial data and supporting decision-making in pharmaceutical research 4710.

Conclusion

Gradient boosting techniques are central to modern artificial intelligence, offering robust, accurate, and scalable solutions for a wide range of applications. Advances in algorithm design, integration with neural networks, and the development of explainable AI tools continue to expand their utility and accessibility. As research progresses, gradient boosting remains a foundational tool for both predictive modeling and interpretable AI.

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Most relevant research papers on this topic

Sequential Training of Neural Networks With Gradient Boosting

Gradient boosting effectively trains neural networks in sequential steps, resulting in competitive generalization performance and allowing for on-the-fly adaptation to different classification speed requirements.

2019·

34citations

·Seyedsaman Emami et al.

IEEE Access·

DOI

A comparative analysis of gradient boosting algorithms

CatBoost is the most accurate gradient boosting algorithm, while LightGBM is the fastest but not the most accurate, and XGBoost is second in both accuracy and training speed.

Highly Cited

2019·

2326citations

·Candice Bentéjac et al.

Artificial Intelligence Review·

DOI

Gradient boosting machines, a tutorial

Gradient boosting machines are powerful machine-learning techniques that can be customized for various applications and can handle model complexity.

Highly Cited

2013·

3376citations

·Alexey Natekin et al.

Frontiers in Neurorobotics·

DOI

Machine learning-based prediction of CFST columns using gradient tree boosting algorithm

The gradient tree boosting algorithm effectively predicts the strength of concrete filled steel tubular (CFST) columns under concentric loading, outperforming other machine learning methods.

Highly Cited

2021·

110citations

·Q. Vu et al.

Composite Structures·

DOI

Implementing local-explainability in Gradient Boosting Trees: Feature Contribution

This paper develops a feature contribution method for Gradient Boost Decision Trees (GBDT) that improves local explainability and reflects its internal behavior, aligning with ethical analysis and GDPR laws.

Preprint

2022·

69citations

·Ángel Delgado-Panadero et al.

ArXiv·

DOI

Gradient Boosting Trees

Gradient boosting is a machine learning technique that optimizes decision trees for regression and classification problems, with the JADBIO framework implementing a convenient subset of the XGBoost framework and extending it to survival optimization.

2020·

26citations

·S. Fafalios et al.

A Tutorial and Use Case Example of the eXtreme Gradient Boosting (XGBoost) Artificial Intelligence Algorithm for Drug Development Applications

The XGBoost algorithm effectively classifies and regressions clinical trial-like datasets, providing valuable insights for drug development.

2025·

67citations

·M. Wiens et al.

Clinical and Translational Science·

DOI

LightGBM: A Highly Efficient Gradient Boosting Decision Tree

LightGBM, a gradient boosting decision tree, speeds up training by up to 20 times while maintaining accuracy, using gradient-based one-side sampling and exclusive feature bundling techniques.

Highly Cited

2017·

14535citations

·Guolin Ke et al.

A novel construction cost prediction model using hybrid natural and light gradient boosting

A hybrid light gradient boosting and natural gradient boosting model provides the most accurate construction cost estimates, with good alignment to actual costs and providing uncertainty estimates through probabilistic predictions.

Highly Cited

2020·

189citations

·Debaditya Chakraborty et al.

Adv. Eng. Informatics·

DOI

Predicting adhesion strength of micropatterned surfaces using gradient boosting models and explainable artificial intelligence visualizations

Gradient-boosting machine learning algorithms accurately predict adhesion strength on micro-patterned surfaces, with explainable machine learning methods providing insights into critical features and interactions.

2023·

31citations

·I. Ekanayake et al.

Materials Today Communications·

DOI

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