用模糊包含度构造超盒粒分类器  

Forming hyperbox granule classifiers by fuzzy inclusion measure

作　　者：刘宏兵[1] 周文勇[1] 熊炎[1] 

机构地区：[1]信阳师范学院计算机与信息技术学院,河南信阳464000

出　　处：《计算机工程与应用》2013年第4期54-57,共4页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61170202);河南省基础研究与前沿技术项目;河南省高校青年骨干教师资助计划(No.2011GGJS-119)

摘　　要：如何将训练集分割成大小不一的超盒粒是粒计算领域的关键问题之一。引入非线性正评价函数并用于构造超盒粒之间的模糊包含度函数,通过粒度阈值,对两个超盒粒有条件合并,构造含有大小不同超盒粒的分类器。实验结果表明超盒粒分类器与模糊格推理分类器相比提高了测试精度,与支持向量机相比加快了训练速度且提高了测试精度。How to divide the training set into the changeable hyperbox granules is one of the key issues in granular computing： Nonlinear positive valuation functions are used to form the fuzzy inclusion measure function between two hyperbox granulesl The classifiers including changeable hyperbox granules are formed by the conditional join strategy, which is determined by the threshold of granularity. The experimental results show that the hyperbox granule classifiers improve the test accuracy compared with fuzzy lattice reasoning classifiers and support vector machines, and speed up the training process compared with support vector machines.

关 键 词：模糊包含度 超盒粒 正评价函数 模糊格推理 支持向量机 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]