Combining Neural Learners with the Na?ve Bayes Fusion Rule for Breast Tissue Classification

摘要：

Early detection of suspicious breast lesions is commonly performed by analysis of breast profiles detected by effective modalities. Tissue distribution in each modality can provide important information about the elastic characteristics of breast which is useful for computer-aided diagnosis. In this paper, the Naïve Bayes (NB) fusion rule is utilized to combine a group of Radial Basis Function (RBF) neural learners in a multiple classifier system for classification of breast tissues. The empirical results show the NB fusion rule may effectively diminish the mean-squared errors, and also improve approximately 15％ classification accuracy, which is significantly better than the component RBF neural learners. Moreover, the NB fusion rule also outperforms the widely used simple average and majority voting fusion rules.

关键词： Naive Bayes rule Breast cancer diagnosis Multiple classier system Neural networks Classification Ensemble.

作者: Yunfeng WU S.C.NG

作者单位: Beijing University of Posts and Telecommunications, China The Open University of Hong KongHong Kong, China

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）