Details
| Original language | English |
|---|---|
| Article number | 16 |
| Number of pages | 20 |
| Journal | Big Data and Cognitive Computing |
| Volume | 8 |
| Issue number | 2 |
| Publication status | Published - 31 Jan 2024 |
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.
Keywords
- class imbalance, discrimination-aware learning, multi-objective optimization, online learning
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Management Information Systems
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Artificial Intelligence
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In: Big Data and Cognitive Computing, Vol. 8, No. 2, 16, 31.01.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Fair-CMNB
T2 - Advancing Fairness-Aware Stream Learning with Naïve Bayes and Multi-Objective Optimization
AU - Badar, Maryam
AU - Fisichella, Marco
N1 - Funding Information: This research was funded by the Lower Saxony Ministry of Science and Culture (Niedersächsische Ministerium für Wissenschaft und Kultur).
PY - 2024/1/31
Y1 - 2024/1/31
N2 - 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.
AB - 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.
KW - class imbalance
KW - discrimination-aware learning
KW - multi-objective optimization
KW - online learning
UR - http://www.scopus.com/inward/record.url?scp=85185542100&partnerID=8YFLogxK
U2 - 10.3390/bdcc8020016
DO - 10.3390/bdcc8020016
M3 - Article
AN - SCOPUS:85185542100
VL - 8
JO - Big Data and Cognitive Computing
JF - Big Data and Cognitive Computing
IS - 2
M1 - 16
ER -