Cascade Generalization: One versus Many
Document Type
Article
Publication Date
3-2016
Abstract
The choice of the best classification algorithm for a specific problem domain has been extensively researched. This issue was also the main motivations behind the ever increasing interest in ensemble methods as well as the choice of ensemble base and meta classifiers. In this paper, we extend and further evaluate a hybrid method for classifiers fusion. The method utilizes two learning algorithms only, in particular; a Support Vector Machine (SVM) as the base-level classifier and a different classification algorithm at the meta-level. This is then followed by a final voting stage. Results on nine benchmark data sets confirm that the proposed algorithm, though simple, is a promising ensemble classifier that compares favorably to other well established techniques.
Recommended Citation
Barakat, Nahla, "Cascade Generalization: One versus Many" (2016). Artificial Intelligence. 11.
https://buescholar.bue.edu.eg/artificial_intelligence/11