| Autor: |
Pelegrina, Guilherme D., Brotto, Renan D. B., Duarte, Leonardo T., Attux, Romis, Romano, João M. T. |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
2022 International Joint Conference on Neural Networks (IJCNN). |
| DOI: |
10.1109/ijcnn55064.2022.9892809 |
| Popis: |
In dimensionality reduction problems, the adopted technique may produce disparities between the representation errors of different groups. For instance, in the projected space, a specific class can be better represented in comparison with another one. In some situations, this unfair result may introduce ethical concerns. Aiming at overcoming this inconvenience, a fairness measure can be considered when performing dimensionality reduction through Principal Component Analysis. However, a solution that increases fairness tends to increase the overall re-construction error. In this context, this paper proposes to address this trade-off by means of a multi-objective-based approach. For this purpose, we adopt a fairness measure associated with the disparity between the representation errors of different groups. Moreover, we investigate if the solution of a classical Principal Component Analysis can be used to find a fair projection. Numerical experiments attest that a fairer result can be achieved with a very small loss in the overall reconstruction error. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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