Using Artificial Immune Algorithm for Fast Convergence of Multi Layer Perceptron in Breast Cancer Diagnosis Application

被引：0

作者：

Daoudi, Rima ^{[1
,2
]}

Djemal, Khalifa ^{[1
]}

Benyettou, Abdelkader ^{[2
]}

机构：

[1] Univ Evry Val dEssonne, IBISC Lab, Evry, France

[2] Univ Sci & Technol Oran, SIMPA Lab, Bir El Djir, Algeria

来源：

5TH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, THEORY, TOOLS AND APPLICATIONS 2015 | 2015年

关键词：

Multi Layer Perceptron; Artificial Immune System; Classification; Breast Cancer; Mean Squared Error; Speed Convergence;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper, a Multi Layer Perceptron (MLP) based Artificial Immune System (AIS) is presented for breast cancer classification. The proposed algorithm integrates clonal selection principle of AIS in MLP learning to reduce its computational costs and accelerate its convergence to a Mean Squared Error Threshold (MSEth) set by the user. Applied on the Wisconsin Diagnosis Breast Cancer database (WDBC), the results show that combining Artificial Immune Systems and Neural Networks is effective. Indeed, a significant reduction of computation time has been obtained with a slight improvement of classification accuracy.

引用

页码：341 / 345

页数：5

共 18 条

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