dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Feelders, A.J. | |
dc.contributor.advisor | Siebes, A.P.J.M. | |
dc.contributor.author | Driel, S. van | |
dc.date.accessioned | 2014-09-16T17:01:04Z | |
dc.date.available | 2014-09-16T17:01:04Z | |
dc.date.issued | 2014 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/18338 | |
dc.description.abstract | Improving the predictive accuracy of rule-based classifiers, specifically the Clark Niblett 2 algorithm, can be done by using an argumentation-based approach. Previous research on this topic focuses on using expert feedback to improve the predictive accuracy. This research has concentrated on providing a working algorithm and limits itself to a few domains which require expert feedback. In combination with writing this thesis an application has been built which makes the practical use of argumentation-based classification a possibility. Experi- ments are included to show the validity of the techniques in the application and to research the viability of the use of argumentation-based classifiers in a num- ber of other domains. The results show that the argumentation-based approach is solid, but possible future research into using ordered rule-based classifiers as opposed to unordered ones can provide greater benefits. The application and experiments strengthen the validity of argumentation-based classification and enhance its practical usage. | |
dc.description.sponsorship | Utrecht University | |
dc.format.extent | 1032548 | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.title | Improving rule-based classification systems by using arguments | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | classification, Clark Niblett 2, rule-based, argumentation, annotation, case-based reasoning, CogniCor, Ruby, Rails, | |
dc.subject.courseuu | Technical Artificial Intelligence | |