An intelligent assessment method of criminal psychological attribution based on unbalance data

Abstract

Criminal cases often exhibit imbalance and cannot be extended by data augmentation when classified into attribution types. To solve the problem of unbalance data in offenders’ attribution classification, this paper proposes a criminal psychological attribution assessment model by an improved Balanced TF-Distinguishing IDF method (B-TF-dIDF) and constructed a hybrid network with attention method to fuse numerical and text features for improving the accuracy. First, as a statistical method, B-TF-dIDF is presented to reduce the impact of class-imbalance for extraction of numerical features, in which a balanced element is added to reduce the effects of incorrect type keywords on classification, and a distinguishing element is added to discriminate the types of keywords. Then, an improved hybrid network model composed of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) is constructed to balance the influence of different lengths of text samples for extracting the semantic features of criminal texts. For evaluating different feature weights by their importance, Spatial Attention is used to improve CNN in the feature maps. Moreover, the self-attention is also performed to re-evaluate the mixed features. Finally, the softmax classifier provides a scientific basis for developing a hierarchical treatment mechanism further. Additionally, we build a criminal data set with labels from real cases for testing. The experiment proved that the proposed model is better than other related methods in various evaluation indicators, including the micro and macro scopes. Moreover, the F1 of minority samples has increased by 6%–8%, indicating that the proposed method can reduce the impact of class-imbalance.