Breast cancer detection using rank nearest neighbor classification rules
In this article, we propose a new generalization of the rank nearest neighbor (RNN) rule for multivariate data for diagnosis of breast cancer. We study the performance of this rule using two well known databases and compare the results with the conventional k-NN rule. We observe that this rule performed remarkably well, and the computational complexity of the proposed k-RNN is much less than the conventional k-NN rule.
breast cancer detection classification rules rank nearest neighbor rules nearest neighbor rules breast masses cell nucleus mean texture worst mean area error rate bayes error rate