Abstract

Breast cancer is the leading disease to cause death especially in women. In this paper, a frame work based algorithm for the classification of cancerous/non-cancerous data is developed using application of supervised machine learning. In feature selection, we derive basis set in the kernel space and then we extend the margin based feature selection algorithm. We are trying to explore several feature selection, extraction techniques and combine the optimal feature subsets with various learning classification methods such as KNN, PNN and Support Vector Machine (SVM) classifiers. The best classification performance for breast cancer diagnosis is attained equal to 99.17% between radius and compact features using SVM classifier. And also derive the features of a breast image in the WBCD dataset.

Authors and Affiliations