Layered Ensemble Learning for Effective Binary Classification
Published in Emerging Technologies in Data Mining and Information Security (IEMIS 2020) – Springer Singapore, 2021
This paper proposes a layered ensemble learning framework that combines multiple base classifiers in a stacked, hierarchical manner to improve binary classification performance. The methodology is validated on benchmark datasets and demonstrates consistent improvement over individual classifiers.
Authors: M. A. Azad, S. Islam, D. M. Farid, S. Shatabda
Venue: IEMIS 2020 → Published in Emerging Technologies in Data Mining and Information Security, Springer Singapore (2021)
Recommended citation: Azad, M. A., Islam, S., Farid, D. M., & Shatabda, S. (2021). "Layered Ensemble Learning for Effective Binary Classification." In Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2020, Volume 3 (pp. 1–9). Springer Singapore. https://link.springer.com/chapter/10.1007/978-981-15-9774-9_1