Show simple item record

dc.contributor.authorWu, Y. F.zh_CN
dc.contributor.author吴云峰zh_CN
dc.contributor.authorNg, S. C.zh_CN
dc.date.accessioned2013-12-12T02:49:29Z
dc.date.available2013-12-12T02:49:29Z
dc.date.issued2013-12-12
dc.identifier.citation2007 Ieee International Joint Conference on Neural2851-2855zh_CN
dc.identifier.issn1098-7576zh_CN
dc.identifier.otherWOS:000254291102132zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/70734
dc.description.abstractThe diagnosis of breast cancer is performed based on informed interpretation of representative histological tissue sections. Tissue distribution detected from cytologic examinations is useful for tumor staging and appropriate treatment. In this paper, we propose a normalized weighted average (Normwave) algorithm for the unbiased linear fusion, and also construct the multiple classifier system that includes a group of Radial Basis Function (RBF) neural classifiers for the classification of breast tissue samples. The empirical results show that the proposed Normwave algorithm may improve the performance of the RBF-based multiple classifier system, and also reliably outperforms some widely used fusion methods, in particular the simple average and adaptive mixture of experts.zh_CN
dc.language.isoen_USzh_CN
dc.subjectCOMBINING CLASSIFIERSzh_CN
dc.subjectAUTO-ASSOCIATORzh_CN
dc.subjectCANCERzh_CN
dc.subjectSTRATEGIESzh_CN
dc.subjectENSEMBLESzh_CN
dc.subjectPATTERNSzh_CN
dc.titleBreast tissue classification based on unbiased linear fusion of neural networks with normalized weighted average algorithmzh_CN
dc.typeArticlezh_CN


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record