Citation

Shen Y, Zhuang W, Wu Q, Qian L. Data Analysis and Knowledge Discovery 2019; 3(11): 70-78.

Copyright

(Copyright © 2019)

DOI

10.11925/infotech.2096-3467.2019.0422

PMID

unavailable

Abstract

[Objective] This study tries to improve the assessment of prison risks, such as violence, suicide, being abetting or abetted. [Methods] We proposed a risk assessment system for prisoners based on the interval-valued fuzzy VIKOR method. First, on the basis of 62-dimesion sample data of more than 1100 prisoner records, we established the optimized data set with interval-valued fuzzy VIKOR method. Then, we trained the new model with multiple machine learning algorithms. Finally, we compared the performance of our model with the existing ones. [Results] The precision, recall and F1 values were improved by 8.9%, 11.1% and 0.1 respectively. [Limitations] We could not propose a universal algorithm for all types of risks. [Conclusions] Our model provides some new directions for prison management and research. © 2019 The Author(s).

Language: zh

Keywords

Risk assessment; Risk Assessment; Learning algorithms; Machine learning; Machine Learning; Assessment system; Data set; Fuzzy VIKOR; Interval-valued; Machine-learning; Multiple machine; Prisoner characteristic; Prisoner Characteristics; Risks assessments; Sample data; the Interval-Valued Fuzzy VIKOR; The interval-valued fuzzy VIKOR