Please wait a minute...
Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (11): 70-78    DOI: 10.11925/infotech.2096-3467.2019.0422
Current Issue | Archive | Adv Search |
Risk Assessment System for Prisons Based on Interval-valued Fuzzy VIKOR Method
Yang Shen1(),Weichao Zhuang1,Qinghua Wu2,Lingfei Qian1
1 College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210006, China
2 Wuxi Zhuoxin Information Technology Co., Ltd, Wuxi 214000, China
Download: PDF (1001 KB)   HTML ( 7
Export: BibTeX | EndNote (RIS)      
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.

Key wordsPrisoner      Characteristics      Risk      Assessment      the      Interval-Valued      Fuzzy      VIKOR      Machine      Learning     
Received: 22 April 2019      Published: 18 December 2019
ZTFLH:  TP393  
Corresponding Authors: Yang Shen     E-mail: shen.y@nuaa.edu.cn

Cite this article:

Yang Shen,Weichao Zhuang,Qinghua Wu,Lingfei Qian. Risk Assessment System for Prisons Based on Interval-valued Fuzzy VIKOR Method. Data Analysis and Knowledge Discovery, 2019, 3(11): 70-78.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0422     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I11/70

比较项 暴力型监犯 心理问题型监犯 教唆他人型监犯 缺乏辨识型监犯
性格特征 脾气暴躁 心理压力大 善于观察利用他人 文化素质低、缺乏辨识能力
犯罪类型特征 故意杀人、抢劫罪、故意
伤害罪等
强奸、猥亵儿童等 经济型犯罪、诈骗罪、煽动
民族仇恨罪等
窝藏罪、包庇罪、参加极端主
义等
风险类型 监狱暴力 恶意报复、自杀 教唆他人有组织地违法乱纪 参加他人教唆的乱纪活动
一类特征 二类特征
刑期 刑期1-3年
刑期3-5年
刑期5-10年
刑期10-20年
犯罪类型 暴力型犯罪
心理问题型犯罪
教唆他人型犯罪
缺乏辨识型犯罪
职业 从事职业数量
家庭情况 亲属数量
团伙 是否团伙作案
地区 北疆地区
南疆地区
东疆地区
外省地区
婚姻情况 是否已婚
监犯编号 评价专家
编号
暴力倾向
下限
暴力倾向
上限
心理问题
倾向下限
心理问题
倾向上限
教唆他人
倾向下限
教唆他人
倾向上限
缺乏辨识
倾向下限
缺乏辨识
倾向上限
6520015418 1 0.10 0.20 0.25 0.35 0.35 0.45 0.10 0.20
2 0.15 0.25 0.30 0.40 0.40 0.45 0.15 0.25
3 0.10 0.15 0.25 0.40 0.30 0.40 0.05 0.25
4 0.10 0.25 0.25 0.35 0.25 0.45 0.20 0.30
监犯编号 暴力倾向 心理问题倾向 教唆他人倾向 缺乏辨识倾向
6520015418 0.20 0.35 0.43 0.21
机器学习算法 拟合优度R2 关键参数值
神经网络 0.12 activation='relu'
随机森林 0.76 n_estimators=50, max_depth=8
决策树 0.86 random_state=2, max_depth=10
SVM 0.45 C=1
KNN 0.44 n_neighbors=9
XGBoost 0.48 n_estimators=25
监犯编号 暴力型 心理问题型 教唆他人型 缺乏辨识型 平均值
6520017131 0.38 0.06 0.00 0.00 0.11
6520015387 0.62 0.20 0.12 0.14 0.27
6520016620 0.62 0.35 0.12 0.14 0.31
6520007860 0.22 0.25 0.82 0.31 0.40
6520006994 0.58 0.01 0.00 0.07 0.16
监犯犯事 监犯未犯事
预测监犯犯事 161 69
预测监犯未犯事 73 357
监犯犯事 监犯未犯事
预测监犯犯事 187 50
预测监犯未犯事 47 376
[1] 张绍彦 . 中国监狱改革发展的问题和方向[J]. 政法论坛, 2018,36(6):110-116.
[1] ( Zhang Shaoyan . The Issues and Direction of the China’s Prison Reform and Development[J]. Tribune of Political Science and Law, 2018,36(6):110-116.)
[2] 王焕芹, 柴洪艳, 徐广军 , 等. 监狱服刑人员的自杀风险与其心理健康及睡眠质量的关系[J]. 中国健康心理学杂志, 2017,25(8):1198-1202.
[2] ( Wang Huanqin, Chai Hongyan, Xu Guangjun , et al. The Relationship Between Mental Health and Sheep Quality with Suicide Risk in Inmates[J]. China Journal of Health Psychology, 2017,25(8):1198-1202.)
[3] 邬庆祥 . 刑释人员人身危险性的测评研究[J]. 心理科学, 2005,28(1):222-224.
[3] ( Wu Qingxiang . A Research on the Appraisal of the Personal Dangerousness of Persons Released After Completion of a Sentence[J]. Psychological Science, 2005,28(1):222-224.)
[4] 马国富, 王子贤, 马胜利 . 机器学习模型在预测服刑人员再犯罪危险性中的效用分析[J]. 河北大学学报: 自然科学版, 2017,37(4):426-433.
[4] ( Ma Guofu, Wang Zixian, Ma Shengli . Analysis of the Effectiveness of Machine Learning Model in Predicting the Risk of Inmates[J]. Journal of Hebei University: Natural Science Edition, 2017,37(4):426-433.)
[5] 陈大国, 黄宁生, 沈涛 . 新犯违规风险评估研究——以福建省某监狱为例[J]. 河南警察学院学报, 2017,26(6):19-23.
[5] ( Chen Daguo, Huang Ningsheng, Shen Tao . A Preliminary Study on the Risk Assessment of Newly-committed Prisoners’ Violation——A Case Study of Certain Prison in Fujian Province[J]. Journal of Henan Police College, 2017,26(6):19-23.)
[6] 曾赟 . 服刑人员刑满释放前重新犯罪风险预测研究[J]. 法学评论, 2011(6):131-137.
[6] ( Zeng Yun . Study on the Risk Prediction of Reoffending of Prisoners Before Their Release from Prison[J]. Law Review, 2011(6):131-137.)
[7] 辛国恩, 王定辉, 曾小滨 , 等. 监狱内部工作风险评估探析[J]. 河南财经政法大学学报, 2014,29(1):131-139.
[7] ( Xin Guoen, Wang Dinghui, Zeng Xiaobin , et al. An Analysis of Work Risk Assessment Inside the Prison[J]. Journal of Henan University of Economics and Law, 2014,29(1):131-139.)
[8] 曹建路 . 成年服刑人员人身危险性评估体系的建构[D]. 金华: 浙江师范大学, 2013.
[8] ( Cao Jianlu . Construction of Personal Danger Evaluation System on Adult Prisoners[D]. Jinhua: Zhejiang Normal University, 2013.)
[9] 徐英兰 . 罪犯狱内危险度评估量表的研制[D]. 上海: 上海师范大学, 2015.
[9] ( Xu Yinglan . The Research on Preliminary Risk Assessment Scale of Criminals in Prison[D]. Shanghai: Shanghai Normal University, 2015.)
[10] Sayadi M K, Heydari M, Shahanaghi K . Extension of VIKOR Method for Decision Making Problem with Interval Numbers[J]. Applied Mathematical Modelling, 2009,33(5):2257-2262.
doi: 10.1016/j.apm.2008.06.002
[11] Opricovic S . Multi-Criteria Optimization of Civil Engineering Systems[D]. University of Belgrade, 1998.
[12] Biswas T K . A Fuzzy-based Risk Assessment Methodology for Construction Project Under Epistemic Uncertainty[J]. International Journal of Fuzzy Systems, 2019,21(4):1221-1240.
doi: 10.1007/s40815-018-00602-w
[13] 耿秀丽, 叶春明 . 基于直觉模糊VIKOR的服务供应商评价方法[J]. 工业工程与管理, 2014,19(3):18-25.
[13] ( Geng Xiuli, Ye Chunming . A Service Supplier Evaluation Approach Based on VIKOR with Vague Set[J]. Industrial Engineering and Management, 2014,19(3):18-25.)
[14] 潘亚虹, 耿秀丽 . 一种基于VIKOR的混合多属性群决策方法[J]. 机械设计与研究, 2018,34(1):177-182.
[14] ( Pan Yahong, Geng Xiuli . A Hybrid Multiple Attributes Group Decision Making Method Based on VIKOR[J]. Machine Design & Research, 2018,34(1):177-182.)
[15] 郭强华, 罗锋, 俞立平 . 基于改进的VIKOR科技评价方法研究——直线距离因子多准则妥协解法LDF-VIKOR[J]. 情报杂志, 2018,37(4):171-175.
[15] ( Guo Qianghua, Luo Feng, Yu Liping . Research on Evaluation of Science and Technology Based on Improved VIKOR——Linear Distance Factor VIKOR[J]. Journal of Intelligence, 2018,37(4):171-175.)
[1] Wang Hanxue,Cui Wenjuan,Zhou Yuanchun,Du Yi. Identifying Pathogens of Foodborne Diseases with Machine Learning[J]. 数据分析与知识发现, 2021, 5(9): 54-62.
[2] Chen Donghua,Zhao Hongmei,Shang Xiaopu,Zhang Runtong. Optimizing Large Hospital Operating Rooms with Data Analytics[J]. 数据分析与知识发现, 2021, 5(9): 115-128.
[3] Li Wenna,Zhang Zhixiong. Research on Knowledge Base Error Detection Method Based on Confidence Learning[J]. 数据分析与知识发现, 2021, 5(9): 1-9.
[4] Che Hongxin,Wang Tong,Wang Wei. Comparing Prediction Models for Prostate Cancer[J]. 数据分析与知识发现, 2021, 5(9): 107-114.
[5] Zhou Zeyu,Wang Hao,Zhao Zibo,Li Yueyan,Zhang Xiaoqin. Construction and Application of GCN Model for Text Classification with Associated Information[J]. 数据分析与知识发现, 2021, 5(9): 31-41.
[6] Ma Jiangwei, Lv Xueqiang, You Xindong, Xiao Gang, Han Junmei. Extracting Relationship Among Military Domains with BERT and Relation Position Features[J]. 数据分析与知识发现, 2021, 5(8): 1-12.
[7] Chai Qingfeng, Shi Linyan, Mei Shan, Xiong Haitao, He Huixin. Extracting Knowledge Elements of Sci-Tech Literature Based on Artificial and Machine Features[J]. 数据分析与知识发现, 2021, 5(8): 132-144.
[8] Jiang Yaren, Le Xiaoqiu. Continual Learning for One-to-many Entity Relationship Generation with Small Samples[J]. 数据分析与知识发现, 2021, 5(8): 45-53.
[9] Su Qiang, Hou Xiaoli, Zou Ni. Predicting Surgical Infections Based on Machine Learning[J]. 数据分析与知识发现, 2021, 5(8): 65-75.
[10] Xu Liangchen, Guo Chonghui. Predicting Survival Rates for Gastric Cancer Based on Ensemble Learning[J]. 数据分析与知识发现, 2021, 5(8): 86-99.
[11] Xu Yuemei, Wang Zihou, Wu Zixin. Predicting Stock Trends with CNN-BiLSTM Based Multi-Feature Integration Model[J]. 数据分析与知识发现, 2021, 5(7): 126-138.
[12] Liu Wenbin, He Yanqing, Wu Zhenfeng, Dong Cheng. Sentence Alignment Method Based on BERT and Multi-similarity Fusion[J]. 数据分析与知识发现, 2021, 5(7): 48-58.
[13] Yang Hanxun, Zhou Dequn, Ma Jing, Luo Yongcong. Detecting Rumors with Uncertain Loss and Task-level Attention Mechanism[J]. 数据分析与知识发现, 2021, 5(7): 101-110.
[14] Lu Quan, He Chao, Chen Jing, Tian Min, Liu Ting. A Multi-Label Classification Model with Two-Stage Transfer Learning[J]. 数据分析与知识发现, 2021, 5(7): 91-100.
[15] Zhang Le, Leng Jidong, Lv Xueqiang, Cui Zhuo, Wang Lei, You Xindong. RLCPAR: A Rewriting Model for Chinese Patent Abstracts Based on Reinforcement Learning[J]. 数据分析与知识发现, 2021, 5(7): 59-69.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn