APA (7th ed.) Citation

Kaden, M., Schubert, R., Voigt, J., Reuss, L., Engelsberger, A., Lövdal, S., . . . Villmann, T. (2026). FA(IR)2MA-GLVQ – A hidden-feature-bias mitigation approach for fairness in classification learning based on generalized matrix learning vector quantization. Neurocomputing, 678, N.PAG. https://doi.org/10.1016/j.neucom.2026.133200

Chicago Style (17th ed.) Citation

Kaden, Marika, Ronny Schubert, Julius Voigt, Lynn Reuss, Alexander Engelsberger, Sofie Lövdal, Elina L. van den Brandhof, Michael Biehl, and Thomas Villmann. "FA(IR)2MA-GLVQ – A Hidden-feature-bias Mitigation Approach for Fairness in Classification Learning Based on Generalized Matrix Learning Vector Quantization." Neurocomputing 678 (2026): N.PAG. https://doi.org/10.1016/j.neucom.2026.133200.

MLA (9th ed.) Citation

Kaden, Marika, et al. "FA(IR)2MA-GLVQ – A Hidden-feature-bias Mitigation Approach for Fairness in Classification Learning Based on Generalized Matrix Learning Vector Quantization." Neurocomputing, vol. 678, 2026, p. N.PAG, https://doi.org/10.1016/j.neucom.2026.133200.