%0 Journal Article %A GU Tong %A LI Jiahao %A LI Wanlin %A LUO Jiangtao %A WANG Zhiyuan %A XU Guoliang %T Intelligent house price evaluation model based on ensemble LightGBM and Bayesian optimization strategy %D 2020 %R 10.11772/j.issn.1001-9081.2019122249 %J Journal of Computer Applications %P 2762-2767 %V 40 %N 9 %X Concerning the problems in traditional house price evaluation method, such as single data source, over-reliance on subjective experience, idealization of considerations, an intelligent evaluation method based on multi-source data and ensemble learning was proposed. First, feature set was constructed from multi-source data, and the optimal feature subset was extracted using Pearson correlation coefficient and sequential forward selection method. Then, with Bagging ensemble strategy used as a combination method, multiple Light Gradient Boosting Machines (LightGBMs) were integrated based on the constructed features, and the model was optimized by using Bayesian optimization algorithm. Finally, this method was applied to the problem of house price evaluation, and the intelligent evaluation of house prices was realized. Experimental results on the real house price dataset show that, compared with traditional models such as Support Vector Machine (SVM) and random forest, the new model introduced with ensemble learning and Bayesian optimization improves the evaluation accuracy by 3.15%, and the evaluation results with percent error within 10% account for 84.09%. It can be seen that, the proposed model can be well applied to the field of intelligent house price evaluation, and has more accurate evaluation results. %U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2019122249