邻域近似条件熵的特定类属性约简及启发算法  

Class-Specific Attribute Reduct and Its Heuristic Algorithm of Neighborhood Approximation Condition-Entropy

作　　者：牟恩 张贤勇[2,3] 姚岳松 邓切 MOU En;ZHANG Xianyong;YAO Yuesong;DENG Qie(College of Medical Information and Engineering,Southwest Medical University,Luzhou,Sichuan 646000,China;Institute of Intelligent Information and Quantum Information,Sichuan Normal University,Chengdu 610066,China;School of Mathematical Sciences,Sichuan Normal University,Chengdu 610066,China)

机构地区：[1]西南医科大学医学信息与工程学院,四川泸州646000 [2]四川师范大学智能信息与量子信息研究所,成都610066 [3]四川师范大学数学科学学院,成都610066

出　　处：《计算机工程与应用》2020年第24期175-180,共6页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61673285);四川省科技基金(No.19YYJC2845);四川省青年基金(No.2017JQ0046);西南医科大学教改项目(No.0901/01040983)。

摘　　要：针对属性约简的推广,采用邻域近似条件熵,建立邻域粗糙集的特定类属性约简及其启发算法。粒化分解决策分类的高层邻域近似条件熵,提取定义特定类的中层邻域近似条件熵并得到上下界与粒化非单调性;提出基于邻域近似条件熵的特定类属性约简,设计启发式约简算法;采用决策表实例与数据集实验进行有效验证。所得结果有利于特定类模式识别的不确定性度量与优化应用。Aiming at generalization of attribute reducts,the class-specific attribute reduct and its heuristic algorithm are established by neighborhood approximation condition-entropy.High classification-based neighborhood approximation condition-entropy is decomposed,and middle class-specific neighborhood approximation condition-entropy is extracted to achieve its double bounds and granulation non-monotonicity.Based on the new information measure,the class-specific attribute reduct is proposed,and a heuristic algorithm is accordingly designed.The relevant validity is finally verified by decision table examples and data set experiments.The obtained results are useful for uncertainty measurement and optimization application of class-specific pattern recognition.

关 键 词：属性约简 特定类属性约简 启发式约简算法 邻域粗糙集 邻域近似条件熵 粒计算 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]