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.

The redundant features of high-dimensional data affect the training efficiency and generalization ability of machine learning. In order to improve the accuracy of pattern recognition and reduce the computational complexity, an unsupervised feature selection method based on Regularized Mutual Representation (RMR) property was proposed. Firstly, the correlations between features were utilized to establish a mathematical model for unsupervised feature selection constrained by Frobenius norm. Then, a divide-and-conquer ridge regression optimization algorithm was designed to quickly optimize the model. Finally, the importances of the features were jointly evaluated according to the optimal solution to the model, and a representative feature subset was selected from the original data. On the clustering accuracy, RMR method is improved by 7 percentage points compared with the Laplacian method, improved by 7 percentage points compared with the Nonnegative Discriminative Feature Selection (NDFS) method, improved by 6 percentage points compared with the Regularized Self-Representation (RSR) method, and improved by 3 percentage points compared with the Self-Representation Feature Selection (SR_FS) method. On the redundancy rate, RMR method is reduced by 10 percentage points compared with the Laplacian method, reduced by 7 percentage points compared with the NDFS method, reduced by 3 percentage points compared with the RSR method, and reduced by 2 percentage points compared with the SR_FS method. The experimental results show that RMR method can effectively select important features, reduce redundancy rate of data and improve clustering accuracy of samples.