Theft Prediction Method Based on Ensemble Features Selection

Theft crime is a difficult problem which shows a high occurrence and low breaking situation. It is an effective way to prevent the crime by predicting the cases in advance. So a new method was proposed based on bagging, following standards of accuracy and differences in feature selections, with the principle of high accuracy rate and difference rate. Heterogeneous learners were used to construct an ensemble learner to identify the occurrence factors, then the efficiency crime prediction was improved with less dimensions of factors. The results show that the proposed SEFV_Bagging algorithm can provide better generalization ability and stability, also its prediction accuracy is better. In addition, the algorithm needn't transcendental knowledge to set the feature subset dimensions manually, which shows obvious advantages in the application of criminal data analysis and forecasting.