PERFORMANCE EVALUATION OF BREAST CANCER DIAGNOSIS USING RADIAL BASIS FUNCTION, C4.5 AND ADABOOST
Journal Title: Scientific Bulletin, Series: Electronics and Computer Science - Year 2017, Vol 17, Issue 2
Abstract
This 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 when three features are removed proved to be the best algorithm in classifying breast cancer.
Authors and Affiliations
A. O. Ameen, M. Olagunju, Awotunde J. B. , T. O. Adebakin, I. O. Alabi
Keywords
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