A New Model for Predicting the Attributes of Suspects

Chuyue Zhang1, Xiaofan Zhao1, Manchun Cai1, Dawei Wang2 and Luzhe Cao1

  1. School of Information Technology and Cyber Security, People’s Public Security University of China
    Beijing, China
  2. College of Criminology, People’s Public Security University of China
    Beijing, China


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.

Key words

crime prediction, suspect prediction, machine learning, deep learning

Digital Object Identifier (DOI)


Publication information

Volume 17, Issue 3 (October 2020)
Year of Publication: 2020
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Zhang, C., Zhao, X., Cai, M., Wang, D., Cao, L.: A New Model for Predicting the Attributes of Suspects. Computer Science and Information Systems, Vol. 17, No. 3, 705-715. (2020), https://doi.org/10.2298/CSIS200107016Z