A new optimal approach using NSVC for Breast Cancer Diagnosis Classification M. Ngadi 1 , A. Amine 2 , H. Hachimi 3 , A. El-Attar 4 1 Systems Engineering Laboratory National School of Applied Sciences Ibn Tofail University of Kenitra - Morocco +212-652-896919 Ngadi.mohammed@univ-ibntofail.ac.ma 2 Researcher Professor of Computer Science National School of Applied Sciences Ibn Tofail University of Kenitra - Morocco +212-668-509922 amine aouatif@univ-ibntofail.ac.ma 3 Researcher Professor of Applied Mathematics. National School of Applied Sciences Ibn Tofail University of Kenitra - Morocco +212-667-884931 hanaa.hachimi@univ-ibntofail.ac.ma 4 PhD in Computer Science University of Sidi Mohammed Ben Abdellah - Morocco. +212-649-302275 adnane.elattar@usmba.ac.ma ABSTRACT Given the enormous number of mammograms performed during last years, computer- based diagnosis of breast cancer turned into a necessity. In particular, the diagnosis of breast masses and their classification currently arouse great interest. Indeed, the complex- ity 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 con- duct 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). Keywords: SVM, NSVC, Classification,Breast Cancer Diagnosis. Mathematics Subject Classification Number : 47N30 International Journal of Imaging and Robotics, [Formerly known as the “International Journal of Imaging” (ISSN 0974-0627)] Volume 16; Issue No. 4; Year 2016; Int. J. Imag. Robot. ISSN 2231–525X; Copyright © 2016 [International Journal of Imaging and Robotics] ISSN 2231–525X www.ceser.in/ceserp www.ceserp.com/cp-jour