Optimizing Configuration of Neural Ensemble Network for Breast Cancer Diagnosis

被引:0
|
作者
Mc Leod, Peter [1 ]
Verma, Brijesh [1 ]
Zhang, Mengjie [2 ]
机构
[1] Cent Queensland Univ, Sch Engn & Technol, Rockhampton, Qld 4702, Australia
[2] Victoria Univ, Sch Engn & Comp Sci, Wellington, New Zealand
来源
PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2014年
关键词
ensemble; breast cancer; digital mammogram; feed forward neural network;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Determining the best values for the parameters of a classifier is a challenge. This challenge is compounded for ensembles. This research evaluates the number of neurons for candidate networks and the number of committee members in our work on variable neural classifiers for breast cancer diagnosis. The evaluation reveals that good neural network accuracy can be achieved with a small number of neurons in the hidden layer and three committee members in the ensemble. The proposed methodology is tested on two benchmark databases achieving 99% classification accuracy.
引用
收藏
页码:1087 / 1092
页数:6
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