dc.identifier.uri |
http://dx.doi.org/10.15488/16732 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/16859 |
|
dc.contributor.author |
Badar, Maryam
|
|
dc.contributor.author |
Fisichella, Marco
|
|
dc.date.accessioned |
2024-03-22T06:52:44Z |
|
dc.date.available |
2024-03-22T06:52:44Z |
|
dc.date.issued |
2024 |
|
dc.identifier.citation |
Badar, M.; Fisichella, M.: Fair-CMNB: Advancing Fairness-Aware Stream Learning with Naïve Bayes and Multi-Objective Optimization. In: Big Data and Cognitive Computing 8 (2024), Nr. 2, 16. DOI: https://doi.org/10.3390/bdcc8020016 |
|
dc.description.abstract |
Fairness-aware mining of data streams is a challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to replace humans in critical decision-making processes, e.g., hiring staff, assessing credit risk, etc. This calls for handling massive amounts of incoming information with minimal response delay while ensuring fair and high-quality decisions. Although deep learning has achieved success in various domains, its computational complexity may hinder real-time processing, making traditional algorithms more suitable. In this context, we propose a novel adaptation of Naïve Bayes to mitigate discrimination embedded in the streams while maintaining high predictive performance through multi-objective optimization (MOO). Class imbalance is an inherent problem in discrimination-aware learning paradigms. To deal with class imbalance, we propose a dynamic instance weighting module that gives more importance to new instances and less importance to obsolete instances based on their membership in a minority or majority class. We have conducted experiments on a range of streaming and static datasets and concluded that our proposed methodology outperforms existing state-of-the-art (SoTA) fairness-aware methods in terms of both discrimination score and balanced accuracy. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Basel : MDPI |
|
dc.relation.ispartofseries |
Big Data and Cognitive Computing 8 (2024), Nr. 2 |
|
dc.rights |
CC BY 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0 |
|
dc.subject |
class imbalance |
eng |
dc.subject |
discrimination-aware learning |
eng |
dc.subject |
multi-objective optimization |
eng |
dc.subject |
online learning |
eng |
dc.subject.ddc |
004 | Informatik
|
|
dc.title |
Fair-CMNB: Advancing Fairness-Aware Stream Learning with Naïve Bayes and Multi-Objective Optimization |
eng |
dc.type |
Article |
|
dc.type |
Text |
|
dc.relation.essn |
2504-2289 |
|
dc.relation.doi |
https://doi.org/10.3390/bdcc8020016 |
|
dc.bibliographicCitation.issue |
2 |
|
dc.bibliographicCitation.volume |
8 |
|
dc.bibliographicCitation.firstPage |
16 |
|
dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|