Early Identification of Youth at Risk of Long Term Emergency Homeless Shelter Use: An Evaluation of Interpretable Machine Learning models
| dc.contributor.advisor | Messier, Geoffrey | |
| dc.contributor.author | Annaa, Osman Jakpa | |
| dc.contributor.committeemember | Yanushkevich, Svetlana | |
| dc.contributor.committeemember | Barcomb, Ann | |
| dc.date | 2024 | |
| dc.date.accessioned | 2023-12-18T20:45:49Z | |
| dc.date.available | 2023-12-18T20:45:49Z | |
| dc.date.issued | 2023-12-18 | |
| dc.description.abstract | Homelessness is a serious violation of one’s dignity, and youth who are long-term shelter users are particularly vulnerable members of a vulnerable demographic. The commitment to prevent and eliminate homelessness, particularly among the youth, is a shared responsibility. Programmes aiming at providing homeless people with permanent housing, mostly identify people who have lived with the condition for an extended period of time for support. Allowing young people to be homeless for an extended period of time before intervening, exposes them to several kinds of hardships on the streets. Early identification of youth at risk of becoming a long term shelter user is a proactive and a more humane way of addressing the problem. Machine learning is brought forth as a tool to augment the expertise of shelter staff in identifying youth at risk of long-term shelter use. Machine learning algorithms are utilised to predict youth at risk of long-term shelter use with the clients’ first 30, 60, 90, 120, or 180 days of shelter access records. A real time program delivery approach was incorporated in the experiments as a supplement to existing other methods in fighting homelessness. Interpretable machine learning models capable of ultimately producing classification rules in DNF format are evaluated. The level of control over the complexity of the generated rules, coupled with statistical evaluation metrics are employed in the evaluation. | |
| dc.identifier.citation | Annaa, O. J. (2023). Early identification of youth at risk of long term emergency homeless shelter use: an evaluation of interpretable machine learning models (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | |
| dc.identifier.uri | https://hdl.handle.net/1880/117759 | |
| dc.language.iso | en | |
| dc.publisher.faculty | Graduate Studies | |
| dc.publisher.institution | University of Calgary | |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | |
| dc.subject | AI | |
| dc.subject | Machine Learning | |
| dc.subject | Homelessness | |
| dc.subject | Interpretable | |
| dc.subject | Youth | |
| dc.subject | Data science | |
| dc.subject.classification | Engineering--Electronics and Electrical | |
| dc.title | Early Identification of Youth at Risk of Long Term Emergency Homeless Shelter Use: An Evaluation of Interpretable Machine Learning models | |
| dc.type | master thesis | |
| thesis.degree.discipline | Engineering – Electrical & Computer | |
| thesis.degree.grantor | University of Calgary | |
| thesis.degree.name | Master of Science (MSc) | |
| ucalgary.thesis.accesssetbystudent | I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible. |