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Massive Crime Data Screening Framework based on Downsampling and Compressed Sensing

Massive crime data screening framework based on downsampling and compressed sensing is implemented in this research work. By selecting an appropriate kernel mapping and forming a sparse representation of the signal in the feature space, the least squares method can be used to then obtain an analytical solution, which is called kernel compressed sensing. The paper proposed the novelties as follows. First, compared with the shortcomings of traditional methods, big data has opened up a new direction for the crime prediction. The big data model is applied to the information pattern analysis. Second, random down-sampling algorithm is employed to randomly select some samples of the majority class and removes these samples from the majority class to adjust the balance of the original data set. The model is simulated on the public data set to validate the performance. The simulation compared with the latest model has also proven the satisfactory results.