基于可变容差关系的变精度粗糙集模型

针对已有不完备信息系统扩展粗糙集模型对噪声鲁棒性差的局限性,首先分析了调节基本知识粒大小的同时引入相对错误分类度的必要性;然后结合系统属性值的缺失定义了对象联系度权值矩阵,并以此为基础提出了基于可变容差关系的变精度粗糙集模型(VPRS-VPTR);接着讨论了模型的性质,分析了模型中相关参数(基本知识粒大小、相对错误分类度)对分类精度的影响,给出了分类精度随模型中相关参数变化的求解算法与时间复杂度分析;最后通过仿真实验与相关研究的扩展粗糙集模型进行对比。仿真结果显示,VPRS-VPTR分类精度更高,而且针对UCI数据库上的几组不完备数据集进行仿真实验的结果还表明,相同参数下各不完备数据集的测试集和训练集分类精度变化趋势相同,进而验证了模型的有效性、灵活性及所提算法的可行性。

Variable precision rough set model based on variable-precision tolerance relation

Focusing on the underdeveloped robustness when the existing extended rough set model encounters the noise for the incomplete information system, the necessity of adjusting the size of basic knowledge granule as well as introducing the relative degree of misclassification was analyzed. Then the Variable Precision Rough Set model based on Variable-Precision Tolerance Relation (VPRS-VPTR) was established on the basis of the object connection weight matrix, which was proposed according to the lack probability of system attribute value. Moreover, the properties of the VPRS-VPTR model were discussed, the classification accuracy under the basic knowledge granule size and the relative degree of misclassification was analyzed, the corresponding algorithm was depicted and the time complexity analysis was given afterwards. The experimental results show that the VPRS-VPTR model has higher classification accuracy compared with some other research about the expanded rough set, and the change trend of the classification accuracy is similar for the train set and the test set of several groups of incomplete data sets in UCI database. It proves that the proposed model is more precise and flexible, and the algorithm is feasible and effective.