article · Journal Of Big Data
Gradient Boosting Trees represent an established ensemble machine learning technique that trains multiple weak learners sequentially to solve prediction problems. A hybrid method integrates Gradient Boosting Trees with clustering algorithms, specifically K-means and Bisecting K-means, to enhance predictive power on regression tasks. This combined strategy was evaluated across forty regression datasets obtained from the UCI and Kaggle repositories. The approach achieved greater efficiency than relying on a single Gradient Boosting Tree model. In addition, statistical assessments employing the Friedman test and the Wilcoxon signed-rank test confirmed that the performance improvements of this hybrid clustering framework are statistically significant compared to the standard model.
Regression models are essential tools for estimating continuous numerical outcomes across computational tasks. By combining gradient boosting with clustering methods, this framework reliably boosts predictive accuracy across diverse datasets. Demonstrating verified performance gains on dozens of standard benchmarks gives developers and researchers a more effective computational technique for complex numerical forecasting.
The work represents early-stage algorithmic research tested solely on public benchmark datasets. It could enable more accurate numerical forecasting tools for software developers and data scientists building analytics platforms. However, the abstract does not indicate any field testing, industry partnerships, or an immediate commercialisation pathway beyond benchmark validation.
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Abstract Gradient Boosting Trees (GBT) is a powerful machine learning technique that is based on ensemble learning methods that leverage the idea of boosting. GBT combines multiple weak learners sequentially to boost its prediction power proving its outstanding efficiency in many problems, and hence it is now considered one of the top techniques used to solve prediction problems. In this paper, a hybrid approach is proposed that combines GBT with K-means and Bisecting K-means clustering to enhance the predictive power of the approach on regression datasets. The proposed approach is applied on 40 regression datasets from UCI and Kaggle websites and it achieves better efficiency than using only one GBT model. Statistical tests are applied, namely, Friedman and Wilcoxon signed-rank tests showing that the proposed approach achieves significant better results than using only one GBT model.
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DOI: 10.1186/s40537-025-01071-3
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