Housing Price Prediction Using a Hybrid Genetic Algorithm with Extreme Gradient Boosting

Siregar, Maria Ulfah and Hardjita, Pahlevi Wahyu and Asdin, Farhan Armawan and Wardani, Dewi and Wijayanto, Ardhi and Yunitasari, Yessi and Anshari, Muhammad (2023) Housing Price Prediction Using a Hybrid Genetic Algorithm with Extreme Gradient Boosting. In: The 2022 International Conference on Computer, Control, Informatics and Its Applications (IC3INA), 22-23 November 2022, Online.

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Official URL: https://dl.acm.org/doi/abs/10.1145/3575882.3575939


Predicting property prices provides a better service for customers to evaluate and estimate price movement before their purchases. Some features including OverallQual and GrLivArea, which were selected when applying GA, become important features that can influence property prices. This research proposes a hybrid Genetic algorithm combined with the Extreme Gradient Boosting algorithm to predict real estate housing prices. The proposed scheme is evaluated by Root Mean Square Error, processing time, and the number of deleted features. The proposed scheme has been compared with the sole Extreme Gradient Boosting. The experimental results show that the proposed scheme produces the smallest root mean square error value of 0.129 compared to 0.133 of the sole Extreme Gradient Boosting. Furthermore, the predicted time of the proposed scheme is much better than the sole method.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: featureselection, RMSE, crossover, mutation
Subjects: Tehnik Informatika
Divisions: Fakultas Sains dan Teknologi > Informatika (S2)
Depositing User: Maria Ulfah Siregar S.Kom. MIT., Ph.D.
Date Deposited: 30 Mar 2023 22:07
Last Modified: 30 Mar 2023 22:07
URI: http://digilib.uin-suka.ac.id/id/eprint/57596

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