Recidivism early warning model based on rough sets and the improved K-prototype clustering algorithm and a back propagation neural network

Kangshun LI, Ziming WANG, Xin YAO, Jiahao LIU, Hongming FANG, Yishu LEI

Research output: Journal PublicationsJournal Article (refereed)peer-review

1 Citation (Scopus)

Abstract

The rate of recidivism by criminals after their release from prison is high, which is harmful to society. Thus, it is socially significant to reduce their recidivism rate. This article uses public data from the state of Iowa in the United States. According to the data characteristics, such as having redundant samples and mixed attributes, we propose the following methods. First, we use a rough set attribute reduction algorithm based on probability distributions to reduce the redundant items. Second, the sample data are clustered with an improved clustering algorithm. Based on the traditional K-prototype clustering algorithm, the clustering algorithm is improved by changing the measurement method of the categorical attributes, changing the initial cluster center selection method, and weighting the attributes based on the information entropy. The clustering experiment results show that the improved clustering algorithm has a better clustering effect and higher clustering accuracy than the traditional K-prototype clustering algorithm. Finally, a back propagation neural network is used to predict the recidivism probability of the sample processed by the above algorithm. The final experimental results show that the two redundant attributes are successfully reduced by rough sets, which greatly reduces the run time of the model. Compared with the traditional K-prototype clustering algorithm, the improved K-prototype clustering algorithm proposed in this paper has a better effect on the various indicators and objective function. Finally, through neural network prediction, the prediction accuracy of this model reached 87.9%. At the same time, a large number of experiments on benchmark datasets verify the effectiveness of our proposed model. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Original languageEnglish
Pages (from-to)839-851
Number of pages13
JournalJournal of Ambient Intelligence and Humanized Computing
Volume14
Issue number2
Early online date12 Jun 2021
DOIs
Publication statusPublished - Feb 2023
Externally publishedYes

Bibliographical note

This work is supported by National Key R&D Program of China with the Grant no. 2018YFC0831100, Natural Science Foundation of Guangdong Province of China with no. 2020A1515010784, the National Natural Science Foundation of China with the Grant no. 61773296, Foreign Science and Technology Cooperation Program of Guangzhou with the Grant no. 201907010021, Key R&D Program of Guangdong Province with no. 2019B020219003, Foreign Science and Technology Cooperation Program of Huangpu District of Guangzhou with no. 2018GH0, the Major Science and Technology Project in Dongguan with no. 2018215121005.

Keywords

  • Clustering algorithms
  • Feedforward neural networks
  • Recidivism
  • Rough sets

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