Breast Cancer Data Analysis Using Support Vector Machines and Particle Swarm Optimization

被引：0

作者：

Arafi, Ayoub ^{[1
]}

Fajr, Rkia ^{[1
]}

Bouroumi, Abdelazjz ^{[1
]}

机构：

[1] UH2MC, Ben Msik Fac Sci, Informat Proc Lab, Sof Comp & Intelligent Syst Res Team, Casablanca, Morocco

来源：

2014 SECOND WORLD CONFERENCE ON COMPLEX SYSTEMS (WCCS) | 2014年

关键词：

breast cancer; support vector machines; particle swarm optimization; machine learning; performance measure; CLASSIFICATION;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We propose a machine learning method for breast cancer data analysis and classification, based on support vector machines (SVM) and particle swarm optimization (PSO). This method uses SVM as a model for supervised learning with the goal of minimizing generalization errors, and PSO as an optimization technique for automatic determination of the best values of two algorithmic parameters of SVM. Its performance in solving classification and recognition problems is experimentally tested for a real-world benchmark dataset. The experimental results are compared to those provided by four other methods using three different objective measures of performance.

引用

页码：1 / 6

页数：6

共 15 条

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