Evaluation of Breast Cancer Tumor Classification with Unconstrained Functional Networks Classifier

This paper proposes functional networks as an unconstrained classifier scheme for multivariate data to diagnose the breast cancer tumor. The performance of this new technique is measured using two well known databases under the minimum description length criterion, the results are compared with the most common existing classifiers in both computer science and statistics literatures. This new classifier shown reliable and efficient results with better correct classification rate, and much less computational time.

• Type: Conference
• Year: 2006
• Where: AICCSA
• Authors: Emad A. El-Sebakhy, Kanaan A. Faisal, Tarek Helmy, Farag Azzedin, A. Al-Suhaim
• Keywords: AICCSA 2006, Breast Cancer Tumor, Common Existing Classifiers, Hardware, Unconstrained Classifier Scheme