摘要
决策粗糙集是一种基于贝叶斯风险最小化原则的具有一定容忍度的概率粗糙集模型,但当前关于决策粗糙集模型的研究只局限于处理具有离散型数据的信息表.文中将模糊集和决策粗糙集理论相结合,在决策粗糙集模型中计算期望风险损失时,利用模糊隶属度函数代替传统的后验概率求解方法,这样可推导出新的决策规则,进而可高效处理那些包含连续型属性的信息系统.实验表明该方法是可行的,并且可通过调整隶属度函数,达到更佳分类效果.
The decision-theoretic rough set （ DTRS ） is a kind of probabilistic rough set model with certain tolerance based on the Bayesian risk minimization principle .However , the current research on DTRS model is restricted to processing information tables with discrete data .In this paper, the decision-theoretic rough set theory is combined with fuzzy sets , and the fuzzy membership functions are employed to replace the posterior probability calculating method when calculating the expected risk losses in the DTRS model.Thus, the new decision rules are derived to effectively deal with the information system with continuous data . Experiments show that the proposed method is feasible and it has a better classification performance by adjusting the membership functions .
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2014年第8期701-707,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金面上重点项目(No.60105003)
国家自然科学基金面上项目(No.61170180)资助
关键词
决策粗糙集
模糊集
隶属度函数
连续值属性
Decision-Theoretic Rough Set Fuzzy Set Membership Function Continuous Attribute
