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http://repository.futminna.edu.ng:8080/jspui/handle/123456789/16090| Title: | PERFORMANCE EVALUATION OF BREAST CANCER DIAGNOSIS USING RADIAL BASIS FUNCTION, C4.5 AND ADABOOST |
| Authors: | Ameen, A.O. Olagunju, M. Awotunde, J.B. Adebakin, T,O, Alabi, I.O. |
| Keywords: | Breast cancer diagnosis, Classification algorithm Expert System Radial basis function Support vector machines Data mining |
| Issue Date: | Jun-2017 |
| Publisher: | Editura Universitatii din Pitesti |
| Citation: | 6. Ameen A. O., Olagunju M., Awotunde, J. B., Adelakin, T. O. & Alabi, I.O. (2017). Performance evaluation of breast cancer diagnosis using radial basis function, C4.5 and Adaboost. University of Pitesti scientific bulletin electronics and computer science, 17 (2), 1-12. |
| Series/Report no.: | Electronics and Computers Science;Vol 17, Issue 2 |
| Abstract: | T his paper conducted a performance evaluation on the most commonly data mining algorithms: Support Vector Machines (Radial basis function), C4.5 decision tree algorithm and Adaboost, using the two previous algorithms as base classifiers (ensemble approach), on breast cancer diagnostic removing redundant or irrelevant features using Chi-square. Result shows that while C4.5 builds its classification model in a short time, The Adaboost with SVM as its base classifier |
| URI: | http://repository.futminna.edu.ng:8080/jspui/handle/123456789/16090 |
| ISSN: | 2344 – 2166 |
| Appears in Collections: | Information and Media Technology |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Cancer Diag.pdf | 571.17 kB | Adobe PDF | View/Open |
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