INFLUENCE OF DISCRETIZATION IN CLASSIFICATION OF BREAST CANCER DISEASE
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Date
2018
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UNIVERSITY OF PITESTI SCIENTIFIC BULLETIN: ELECTRONICS AND COMPUTERS SCIENCE
Abstract
Breast cancer (BC) is one of the leading cancers for women when compared to all other cancers. It is a killer disease prominent and most frequent type of cancer affecting women worldwide and is increasing particularly in Africa. The aim of this paper is to investigate the influence of data preprocessing based on dicretization in the classification of BC. Two different classification algorithms Support vector machine-Radial basis function (SVM-RBF) and Adaboost algorithm were employed. We analyzed the BC data available from the Wisconsin dataset from UCI machine learning repository. The experiment was performed in Waikato Environment For knowledge analysis (Weka) software. The experimental results showed that discretized SVM-RBF and discretized Adaboost algorithms outperforms the non-discretized SVM-RBF and nondiscretized Adaboost algorithms in terms of accuracy, precision, recall, f-measure and time taken to build the model.
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Keywords
Breast cancer, Support vector machine-Radial basis function, Adaboost, Discretization