Monotone Oblique Decision Trees
| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Feelders, Ad | |
| dc.contributor.author | Sadawi, Maarten Al | |
| dc.date.accessioned | 2023-07-20T00:02:09Z | |
| dc.date.available | 2023-07-20T00:02:09Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/44220 | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | In many data analysis applications, it is often reasonable to assume that the response variable increases or decreases in relation to one or more attributes or features. These relationships between the response and features are referred to as monotone. We have developed an algorithm that enforces monotonicity for oblique classification/regression decision trees and evaluated its performance on well-known datasets for monotone classification, regression, as well as artificially constructed data. | |
| dc.title | Monotone Oblique Decision Trees | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.courseuu | Computing Science | |
| dc.thesis.id | 19512 |
