Thresholds learning of three-way decisions in pairwise crime linkage

被引:11
|
作者
Li, Yusheng [1 ]
Shao, Xueyan [1 ]
机构
[1] Univ Chinese Acad Sci, Sch Publ Policy & Management, Beijing 100049, Peoples R China
关键词
Crime linkage; Three-way decisions; Serial crimes; Decision-theoretic rough set; THEORETIC ROUGH SET; ATTRIBUTE REDUCTION; MAKING APPROACH; OPTIMIZATION; SERIES; MODEL; CLASSIFICATION; APPROXIMATIONS; BURGLARIES; REGRESSION;
D O I
10.1016/j.asoc.2022.108638
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Crime linkage is a difficult task and is of great significance to maintaining social security. It can be treated as a binary classification problem. Some crimes are difficult to determine whether they are serial crimes under the existing evidence, so the two-way decisions are easy to make mistakes for some case pairs. Here, the three-way decisions based on the decision-theoretic rough set are applied and its key issue is to determine thresholds by setting appropriate loss functions. However, sometimes the loss functions are difficult to obtain. In this paper, a method to automatically learn thresholds of the three-way decisions without the need to preset explicit loss functions is proposed. We simplify the loss function matrix according to the characteristic of crime linkage, re-express thresholds by loss functions, and investigate the relationship between overall decision cost and the size of the boundary region. The trade-off between the uncertainty of the boundary region and the decision cost is taken as the optimization objective. We apply multiple traditional classification algorithms as base classifiers, and employ real-world cases and some public datasets to evaluate the effect of our proposed method. The results show that the proposed method can reduce classification errors. (c) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页数:16
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