JCP 2017 Vol.12(3): 238-249 ISSN: 1796-203X
doi: 10.17706/jcp.12.3.238-249
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.
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
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
-
Nov 14, 2019 News!
Vol 14, No 11 has been published with online version [Click]
-
Mar 20, 2020 News!
Vol 15, No 2 has been published with online version [Click]
-
Dec 16, 2019 News!
Vol 14, No 12 has been published with online version [Click]
-
Sep 16, 2019 News!
Vol 14, No 9 has been published with online version [Click]
-
Aug 16, 2019 News!
Vol 14, No 8 has been published with online version [Click]
- Read more>>