Evaluation of breast cancer tumor classification with unconstrained functional networks classifier

被引：12

作者：

El-Sebakhy, Emad A. ^{[1
]}

Faisal, Kanaan Abed ^{[1
]}

Helmy, T. ^{[1
]}

Azzedin, F. ^{[1
]}

Al-Suhaim, A. ^{[1
]}

机构：

[1] King Fahd Univ Petr & Minerals, Coll Comp Sci & Engn, Dept Informat & Comp Sci, Dhahran 31261, Saudi Arabia

来源：

2006 IEEE INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS, VOLS 1-3 | 2006年

关键词：

pattern classification; functional networks; breast cancer detection; minimum description length;

D O I：

10.1109/AICCSA.2006.205102

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：281 / +

页数：3

共 20 条

[11] Neural networks approach to early breast cancer detection [J].