A new optimal approach using NSVC for Breast Cancer Diagnosis Classification
Given the enormous number of mammograms performed during last years, computerbased diagnosis of breast cancer turned into a necessity. In particular, the diagnosis of breast masses and their classification currently arouse great interest. Indeed, the complexity of the processed forms and the difficulty encountered in order to discern them require the use of appropriate descriptors. This article is placed in the context of evaluating the results of supervised classification algorithms and their comparison. In this work, we conduct some experiments using the Wisconsin diagnosis Breast Cancer (WDBC) dataset in order to classify the dataset samples to be either benign or malignant. We show that the
best results are obtained using our new proposed neighboring Support Vector Classifier (NSVC).
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