Evaluation of Data Mining for Two Child-Related, Social Risk Issues
| dc.contributor.author | Lıttle, James | |
| dc.contributor.author | Little, J. | |
| dc.contributor.author | Waheed, H.A. | |
| dc.contributor.author | Rixon, A. | |
| dc.contributor.other | Matematik | |
| dc.date.accessioned | 2025-09-23T12:48:26Z | |
| dc.date.available | 2025-09-23T12:48:26Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | Two child-related social issues are examined using data mining to determine successful ways of predicting risk. The issues of child truancy and child abuse can be considered similar as both are influenced by, the child’s characteristics, family and environment. The results show that from an initial portfolio of algorithms, a one-nearest neighbour approach works well. We believe that reflects the nature of the problem, where expert opinion classifies each new pupil /case in terms of similar ones, while the one-nearest aspect, reflects the small amount of data we had access to. © 2017 CEUR-WS. All rights reserved. | en_US |
| dc.identifier.citation | Little, James; Waheed, Hayder A.; Rixon, Andy (2017). "Evaluation of data mining for two child-related, social risk issues", CEUR Workshop Proceedings, Vol. 2086, pp. 219-231. | en_US |
| dc.identifier.issn | 1613-0073 | |
| dc.identifier.scopus | 2-s2.0-85046039322 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12416/15267 | |
| dc.language.iso | en | en_US |
| dc.publisher | CEUR-WS | en_US |
| dc.relation.ispartof | CEUR Workshop Proceedings -- 25th Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2017 -- 7 December 2017 through 8 December 2017 -- Dublin -- 135794 | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Child Abuse | en_US |
| dc.subject | Data Mining | en_US |
| dc.subject | Risk | en_US |
| dc.subject | Social Ai | en_US |
| dc.subject | Truancy | en_US |
| dc.title | Evaluation of Data Mining for Two Child-Related, Social Risk Issues | en_US |
| dc.title | Evaluation of data mining for two child-related, social risk issues | tr_TR |
| dc.type | Conference Object | en_US |
| dspace.entity.type | Publication | |
| gdc.author.scopusid | 57200233208 | |
| gdc.author.scopusid | 57201748383 | |
| gdc.author.scopusid | 57993931200 | |
| gdc.coar.access | metadata only access | |
| gdc.coar.type | text::conference output | |
| gdc.description.department | Çankaya University | en_US |
| gdc.description.departmenttemp | Little J., Connect Centre, Trinity College Dublin, Ireland; Waheed H.A., Department of Mathematics, Çankaya University, Ankara, Turkey; Rixon A., School of Health, Wellbeing and Social Care, Open University, United Kingdom | en_US |
| gdc.description.endpage | 231 | en_US |
| gdc.description.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | Q4 | |
| gdc.description.startpage | 219 | en_US |
| gdc.description.volume | 2086 | en_US |
| gdc.scopus.citedcount | 0 | |
| gdc.virtual.author | Lıttle, James | |
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