Volume 12 Number 3 (May 2017)
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JCP 2017 Vol.12(3): 238-249 ISSN: 1796-203X
doi: 10.17706/jcp.12.3.238-249

Cascade Generalization: One versus Many

Nahla Barakat
Faculty of Informatics and Computer Science, the British University in Egypt (BUE), Cairo, Egypt.
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 favourably to other well established techniques.

Index Terms—Cascade generalization, classification, ensemble methods, SVM.


Cite: Nahla Barakat, "Cascade Generalization: One versus Many," Journal of Computers vol. 12, no. 3, pp. 238-249, 2017.

General Information

ISSN: 1796-203X
Frequency: Monthly (2006-2014); Bimonthly (Since 2015)
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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