• Korean Journal of Forensic Psychology
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• v.13 no.2
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• pp.99-119
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• 2022

Research on sex offense has shown that sex offenders are very heterogeneous. Sex offenders are heterogeneous in their probability of risk of recidivism. Some sex offenders are known to be much higher in their tendencies to reactivate than others. The study examined the predictive and explanatory power of static and dynamic risk factors in STATIC-99 and HAGSOR-Dynamic which have been used in Korean correctional facilities since 2014. STATIC-99 and HAGSOR-Dynamic showed moderate predictive accuracy for all crimes(AUC = .737, AUC = .597, respectively, ps < .001). However, when examining sex crime alone, only STATIC-99 predicted recidivism significantly(AUC = .743, p < .001). The incremental predictive power of HAGSOR-Dynamic was confirmed; the explanatory power of Model 2 comprising both static and dynamic risk factors were significant beyond Model 1 comprising only static factors(∆χ²= 12.721, p < .001), but this tendency was only applied to the model of all crimes. These findings were discussed with implications of practicing the sex offender assessment and treatment.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.127-137
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• 2019

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.

• Korean Journal of Forensic Psychology
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• v.12 no.1
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• pp.15-33
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• 2021

In this study, we analyzed the difference in survival rates of those subject to electronic supervision of sex crimes based on the tracking of the period of recidivism and whether they were recidivism, and wanted to confirm the ability of the criminal record to predict recidivism. The criteria for recidivism were defined as cases where a conviction was confirmed due to a criminal case that occurred during the execution of electronic monitoring, and the date of recidivism was the date of occurrence of a case that was confirmed guilty. A total of 122 re-offenders were used in the analysis, and all of them were charged with electronic supervision for committing sex crimes. Studies have confirmed that the subjects commit the most recidivism within three years. In addition, in this study, the difference in survival rate between groups was analyzed after classifying mixed and sex recidivism cases. The number of members was 88 for the mixed recidivism group and 34 for the sex recidivism group. The analysis confirmed that both groups had the most recidivism within three years. There was a slight difference between the survival rate of the mixed recidivism group and the survival rate of the sex recidivism group. So the Log Rank Test and the Generalized Wilcoxon Test were conducted, but no statistically significant differences were identified(Wilcoxon statistic = 2.326, df = 1, p = .13, Log Rank = 1.345, df = 1, p = .25). Next, a Cox Regression analysis was performed to confirm the ability of the criminal record to predict recidivism. As a result, the number of criminal records(sex offense, violent crime) have been confirmed to be a good predictor of recidivism(X²=27.33, df=1, p< .001). As a result, the recidivism rate is gradually decreasing due to the implementation of the electronic monitoring. However, the duration of recidivism required by sex offenders in high-risk groups was found to be rather short. Currently, security measures against felons are being strengthened, so it is necessary to select high-risk groups. Therefore, based on the related studies, the characteristics of high-risk groups and the results of recidivism studies will be used as a basis for disposal within the criminal justice system, which will play a major role in granting objectivity.

• 한국ITS학회:학술대회논문집
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• 2003.11a
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• pp.280-285
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• 2003

• Journal of Korean Society of Transportation
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• v.27 no.3
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• pp.149-160
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• 2009

The purpose of this study is to comparatively analyse the behavior patterns of the first and the repeated offenders in DWI, and to develope the models of BAC(Blood Alcohol Concentration) by using multiple regression analysis method and a model of repeated DWI conviction by using logistic regression analysis method. The main results are as follows. First, the repeated offenders are more in criminal and traffic accidents records than that of the first offenders. The unlicenced drivers are in higher BAC than licenced drivers. Second, multiple regression model of BAC was developed, and the model revealed that criminal records and driving distance were important factors. Third, a model of repeated DWI conviction was developed, and the model revealed that traffic accidents records, whether or not having licence, and criminal records were most important factors.

• 도로교통
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• s.94
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• pp.30-40
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• 2003

• Proceedings of the Fisheries Business Administration Society of Korea Conference
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• 2008.06a
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• pp.15-24
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• 2008

• Proceedings of the Korean Society of Fisheries Technology Conference
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• 1997.06a
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• pp.248.2-249
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• 1997

• Korean Journal of Culture and Social Issue
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• v.10 no.2
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• pp.39-77
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• 2004

This literature review introduced the concept of psychopathy which has never been defined academically in Korea. Also it is reviewed how this concept could be applied as latent factor of criminal behavior in the forensic settings. For this purpose, first of all, the periodical change of psychopathy definition was explored. Then it was investigated which determinants might develop psychopathy and what would be the behavioral characteristics of psychopaths. Finally, risk assessment tools measuring psychopathy were introduced and their predictive efficacy and applicability in Korean criminal justice system was discussed.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.1096-1100
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• 2005

음성서비스 수요의 증대와 정보통신 기술의 급속한 발달로 국내 이동통신 시장이 확대되고 있는 오늘날 고객의 요구를 충족시키는 정액요금제도가 절실히 필요하다. 앞으로 상용화되는 위성DMB (Digital Multimedia Broadcasting), 지상파DMB, WiBro 등의 신규 통신서비스는 정액제를 기조로 하고 있다. 따라서 무선데이터 정액제 가입자에 대한 신뢰도 높은 수요예측이 국내 이동통신 사업자에게 매우 중요한 과제로 부각되고 있다. 본 연구에서는 무선데이터 정액제 가입자 수요를 이동통신 시장의 환경에 맞추어 Lotka-Volterra 모형을 확장하여 예측하였다. 무선데이터 정액제 가입자의 수요예측은 이동통신사들이 정액제 도입의 정당성과 도입 시기를 결정하고, 마케팅 전략을 수립하는데 중요한 역할을 할 수 있다. 또한 예측결과는 무선데이터 사업을 평가하는데 기초 자료로서 활용될 것으로 기대된다.