A reinforcement learning-based weight fusion algorithm for house price prediction

To address the limitations of existing house price prediction methods that struggle with handling mixed data types, we developed a novel model utilizing a reinforcement learning-based weight fusion algorithm. First, we reconstruct the feature fusion vectors from categorical feature variables. Then, we create individual house price prediction models using a variety of algorithms: Multiple Linear Regression, Regression Decision Tree, Extreme Random Regression Tree, and Multilayer Perceptron. These models' weights are dynamically adjusted using Q-Learning to minimize the Root Mean Square Error (RMSE). Our experiments demonstrate substantial gains in RMSE performance using our weight fusion approach. Specifically, on the small-scale Shenzhen dataset, RMSE improvements vary from 8.65% to 64.61%. On the large-scale Boston dataset, the improvements range between 53.06% and 76.40% in comparison to the standalone prediction algorithms.