Research on prediction of multi-class theft crimes by an optimized decomposition and fusion method based on XGBoost

The number of theft cases is much higher than that of other criminal cases, which frequently occurs in daily life and is seriously destructive to social order. Studying the law of theft cases has a positive impact on social governance and optimizing police deployment. Therefore, based on the data of theft cases in H city, this study proposes an optimized decomposition and fusion method based on XGBoost, and establishes two multi-classification prediction models, such as OVR-XGBoost and OVO-XGBoost. As the theft data is a datasets with unbalanced class distribution, this paper uses SMOTENN algorithm to process it into a datasets with balanced distribution, which effectively improves the effect of the model. Experiments show that the prediction accuracy of OVR-XGBoost and OVO-XGBoost models is higher than that of baseline XGBoost models. For categories with few samples, the classification effect of OVO-XGBoost is better than that of baseline XGBoost and OVO-XGBoost models. Compared with baseline XGBoost model, the average overall classification accuracy of OVO-XGBoost model is improved by more than 7%, and the MacroR accuracy is also improved by more than 15%. The model proposed in this study has a good effect on the classification and prediction of theft types, and is of great significance for the prevention of theft cases.