PREDICTING LIFE-COURSE PERSISTENT OFFENDING USING MACHINE LEARNING
| dc.contributor.advisor | Zhang, Yan | |
| dc.creator | Oh, Gyeongseok | |
| dc.creator.orcid | 0000-0002-0447-4540 | |
| dc.date.accessioned | 2021-08-16T23:00:20Z | |
| dc.date.available | 2021-08-16T23:00:20Z | |
| dc.date.created | 2021-08 | |
| dc.date.issued | 2021-07-14 | |
| dc.date.submitted | August 2021 | |
| dc.date.updated | 2021-08-16T23:00:21Z | |
| dc.description.abstract | The current study investigated the predictive ability of Life-Course-Persistent (LCP) offenders using Machine Learning techniques. Drawing on the National Longitudinal Survey of Youth 1997, LCP and adolescent limited offenders are identified by the latent class growth analysis. Using seven types of Machine Learning techniques, the LCP offenders are predicted by risk factors verified by previous empirical studies. The results of predictive modeling reveal that the Machine Learning-based prediction of LCP offenders significantly outperforms the conventional parametric statistical analysis, logistic regression. Most of all, the predictive ability of Random Forests and Deep Learning model show a more effective forecasting ability than other Machine Learning- based modeling and logistic regression analysis. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/20.500.11875/3162 | |
| dc.language.iso | en | |
| dc.subject | Machine learning | |
| dc.subject | Life-course persistent offender | |
| dc.subject | Predictive modeling | |
| dc.subject | Developmental criminology | |
| dc.title | PREDICTING LIFE-COURSE PERSISTENT OFFENDING USING MACHINE LEARNING | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.department | Criminal Justice and Criminology | |
| thesis.degree.grantor | Sam Houston State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |
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