Bayesian fairness
| dc.contributor.author | Belfrage, Amanda | |
| dc.contributor.author | Berg Marklund, David | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för data och informationsteknik | sv |
| dc.contributor.examiner | Haghir Chehreghani, Morteza | |
| dc.contributor.supervisor | Dimitrakakis, Christos | |
| dc.date.accessioned | 2020-07-08T10:57:23Z | |
| dc.date.available | 2020-07-08T10:57:23Z | |
| dc.date.issued | 2020 | sv |
| dc.date.submitted | 2020 | |
| dc.description.abstract | This thesis aims to extend the Bayesian fairness algorithm created by Dimitrakakis et al. to be able to handle continuous data. Using bagging to approximate the data we aim to reduce the problem to a computable task that still performs well enough to be an improvement over using the true underlying data. Even though promising results where found for using bagging with discrete data, the continuous version of the algorithm did not work. | sv |
| dc.identifier.coursecode | DATX05 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/301398 | |
| dc.language.iso | eng | sv |
| dc.setspec.uppsok | Technology | |
| dc.subject | Fairness | sv |
| dc.subject | Bayesian fairness | sv |
| dc.subject | algorithm | sv |
| dc.title | Bayesian fairness | sv |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.uppsok | H | |
| local.programme | Computer science – algorithms, languages and logic (MPALG), MSc |