| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Qahtan, A.A.A. | |
| dc.contributor.author | Mariani, S.M. | |
| dc.date.accessioned | 2020-08-24T18:00:40Z | |
| dc.date.available | 2020-08-24T18:00:40Z | |
| dc.date.issued | 2020 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/37006 | |
| dc.description.abstract | Criminal profiling has gained a lot of recognition over the
years. Profiling is done by experts who use information from
a crime scene, to create a serial killer profile. Such a profile
consists of serial killer attributes and can include: the gender, race and possible previous activities of the killer. The
paper proposes a framework that combines multiple wellknows supervised machine learning techniques to create such
a profile. The majority of the proposed approaches obtained
a balanced accuracy over 72%, and a predictive accuracy
over 80%. The proposed approaches also performed well on
a set of other databases, including a single-victim homicide
database where it reached a balanced accuracy over 72% and
a predictive accuracy over 77% | |
| dc.description.sponsorship | Utrecht University | |
| dc.format.extent | 277367 | |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | |
| dc.title | Profiling Serial Killers Using Multiple Supervised Machine Learning
Approaches | |
| dc.type.content | Bachelor Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.keywords | ai, artificial intelligence, machine learning, serial killer, classification, snorkel, classifier ensembles, profiling, serial killer profiling | |
| dc.subject.courseuu | Kunstmatige Intelligentie | |
