Nowadays women are commonly diagnosed with breast cancer. Feature based Selection method plays an important step while constructing a classification based framework. We have proposed Multi filter union (MFU) feature selection method for breast cancer data set. The feature selection process based on random forest algorithm and Logistic regression (LG) algorithm based union model is used for selecting important features in the dataset. The performance of the data analysis is evaluated using optimal features subset from selected dataset. The experiments are computed with data set of Wisconsin diagnostic breast cancer center and next the real data set from women health care center. The result of the proposed approach shows high performance and efficient when comparing with existing feature selection algorithms.

如今，女性通常被诊断出患有乳腺癌。基于特征的选择方法在构建基于分类的框架时起着重要的作用。我们提出了用于乳腺癌数据集的多过滤器联合（MFU）特征选择方法。基于随机森林算法和基于逻辑回归（LG）算法的联合模型的特征选择过程用于选择数据集中的重要特征。使用选定数据集中的最佳特征子集来评估数据分析的性能。实验是用威斯康星州诊断乳腺癌中心的数据集计算的，接下来是来自妇女保健中心的真实数据集。与现有的特征选择算法相比，所提出的方法的结果显示出高性能和高效。