@inproceedings{5d00fc0851844b76a4ec216a7eec10e5,
title = "Evaluation of breast cancer tumor classification with unconstrained functional networks classifier",
abstract = "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.",
keywords = "Breast cancer detection, Functional networks, Minimum description length, Pattern classification",
author = "El-Sebakhy, \{Emad A.\} and Faisal, \{Kanaan Abed\} and T. Helmy and F. Azzedin and A. Al-Suhaim",
year = "2006",
doi = "10.1109/aiccsa.2006.205102",
language = "English",
isbn = "1424402123",
series = "IEEE International Conference on Computer Systems and Applications, 2006",
publisher = "IEEE Computer Society",
pages = "281--287",
booktitle = "IEEE International Conference on Computer Systems and Applications, 2006",
address = "United States",
}