Hybrid Ensemble Learning and AHP for Housing Price Prediction

Authors

  • Hanieh Ghane * Department of Mathematics and Computer Science, Shiraz Branch, Islamic Azad University, Shiraz, Iran.
  • Fateme Fallahzade Department of Mathematics and Computer Science, Shiraz Branch, Islamic Azad University, Shiraz, Iran.
  • Sahar Khoshi Department of Mathematics and Computer Science, Shiraz Branch, Islamic Azad University, Shiraz, Iran.

https://doi.org/10.48314/ijorai.v1i2.59

Abstract

In this paper, a hybrid approach combining ensemble learning and the Analytic Hierarchy Process (AHP) is proposed for selecting the best model to predict housing prices. Initially, three base models—including Random Forest, AdaBoost, and XGBoost—were trained and optimized using GridSearchCV, and their performances were evaluated based on accuracy, training time, and interpretability criteria. Then, the weight of each criterion was systematically determined using AHP to incorporate the relative importance of the criteria in the model selection process. Finally, model weights were obtained based on the weighted scores from these criteria, and a Voting Regressor ensemble model was constructed using these weights. The results showed that although XGBoost achieved the highest accuracy, AdaBoost obtained a higher score within the AHP framework due to its shorter training time and better interpretability. This study demonstrates that the simultaneous use of ensemble learning and AHP can significantly aid in model selection in Machine Learning (ML), especially when multiple and conflicting criteria are involved.     

Keywords:

Ensemble learning, Model selection, Analytic hierarchy process, Housing price prediction

References

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Published

2025-06-15

Issue

Vol. 1 No. 2 (2025): International Journal of Operations Research and Artificial Intelligence (IJORAI)

Section

Articles

How to Cite

Ghane, H. ., Fallahzade, F. ., & Khoshi, S. . (2025). Hybrid Ensemble Learning and AHP for Housing Price Prediction. International Journal of Operations Research and Artificial Intelligence , 1(2), 82-89. https://doi.org/10.48314/ijorai.v1i2.59

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