Express-related Counterfeit Cigarette Crime Prediction with Imbalanced Data-based Machine Learning Techniques

The number of cigarette-related packages and normal packages is often extremely unbalanced in practice, this characteristic must be considered in the express-related counterfeit cigarette crime prediction, otherwise the performance of prediction model on unknown packages will be affected badly. In this paper, an approach of using SMOTE-ENN technology to deal with imbalanced express data and establish the express-related counterfeit cigarette crime prediction model is proposed. In the preprocessing stage, original data sets are structured by Chinese word segmentation method. In feature calculation and analysis stage, the spatio-temporal features of express data are calculated and analyzed, and the optimal features are further obtained by feature reduction algorithm named PCA. Next, based on optimal features, balanced data are obtained by mixed sampling. Finally, the classifier model was trained and optimized to predict the probability of a package containing counterfeit cigarettes in express data including 2851 normal samples and 285 cigarette-related samples randomly selected from 3077 cigarette-related samples. The performance of all classifiers is improved after using the SMOTE_ENN to balance datasets, and the indicators are above 98%. Experimental results demonstrate feasibility, effectiveness of the proposed approach.