A New Model for Predicting the Attributes of Suspects

Chuyue Zhang, Xiaofan Zhao, Manchun Cai, Dawei Wang, Luzhe Cao

In this paper, we propose a new model to predict the age and number of suspects through the feature modeling of historical data. We discrete the case information into values of 20 dimensions. After feature selection, we use 9 machine learning algorithms and Deep Neural Networks to extract the numerical features. In addition, we use Convolutional Neural Networks and Long ShortTerm Memory to extract the text features of case description. These two types of features are fused and fed into fully connected layer and softmax layer. This work is an extension of our short conference proceeding paper. The experimental results show that the new model improved accuracy by 3% in predicting the number of suspects and improved accuracy by 12% in predicting the number of suspects. To the best of our knowledge, it is the first time to combine machine learning and deep learning in crime prediction.