%0 Journal Article
%T Attribute Reduction in Bayesian Rough Set Model for Binary Decision Problems<br>二值决策Bayesian粗糙集模型属性约简研究
%A ZHOU Jie
%A MIAO Duo-qian
%A <br>周杰
%A 苗夺谦
%J 计算机科学
%D 2011
%I 
%X Bayesian rough set model(BRSM) , as the hybrid development between rough set theory and I3ayesian reasoning,can deal with many practical problems which could not be effectively handled by Pawlak's rough set model. The prior probabilities of events are considered as a benchmark when determining the approximation regions in BRSM. In this paper, the equivalence between two kinds of current attribute reduction models in BRSM for binary decision problems,viz. Slezak and Ziarko's modcls,was proved. Furthermore, an associated discernibility matrix for binary decision problems in BRSM was proposed, with which the available attribute reduction methods based on discernibility matrices in the Pawlak's rough set model can be transferred to BRSM.
%K Bayesian rough set model
%K Certainty gain
%K Discernibility matrix
%K Binary decision<br>Bayesian粗糙集模型，置信增益，分辨矩阵，二值决策
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=2AC66EB828B1E435663021623B64FFE2&yid=9377ED8094509821&vid=16D8618C6164A3ED&iid=5D311CA918CA9A03&sid=797D49279EA93BC4&eid=057CA257FD32F0A0&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=0