陈旭生, 刘宏兵, 李为华. 基于距离的粒计算分类算法[J]. 信阳师范学院学报(自然科学版), 2015, 28(2): 271-274. DOI: 10.3969/j.issn.1003-0972.2015.02.028
引用本文: 陈旭生, 刘宏兵, 李为华. 基于距离的粒计算分类算法[J]. 信阳师范学院学报(自然科学版), 2015, 28(2): 271-274. DOI: 10.3969/j.issn.1003-0972.2015.02.028
Chen Xusheng , Liu Hongbing , Li Weihua . Granular Computing Classification Algorithms Based on Distance[J]. Journal of Xinyang Normal University (Natural Science Edition), 2015, 28(2): 271-274. DOI: 10.3969/j.issn.1003-0972.2015.02.028
Citation: Chen Xusheng , Liu Hongbing , Li Weihua . Granular Computing Classification Algorithms Based on Distance[J]. Journal of Xinyang Normal University (Natural Science Edition), 2015, 28(2): 271-274. DOI: 10.3969/j.issn.1003-0972.2015.02.028

基于距离的粒计算分类算法

Granular Computing Classification Algorithms Based on Distance

  • 摘要: 提出了一种基于距离的粒计算分类算法. 首先, 将粒表示为具有超菱形、 超球和超正方体三种形式;第二, 设计两粒之间的合并算子, 实现不同粒度之间的转换;第三, 选取粒度阈值, 控制粒之间的合并过程,并构造基于距离的粒计算分类算法. 使用 UCI 机器学习的基准数据集合验证该算法的性能, 实验结果验证了基于距离的粒计算分类算法的可行性.

     

    Abstract: The granular computing classification algorithms based on distance were proposed. Firstly, granules were represented as the forms of hyperdiamond, hypersphere and hypercube. Secondly, the union operators were designed to realize the transformation between two granule spaces with different granularity. Thirdly, the thresholds of granularity were used to control the union processes. Then the granular computing classification algorithms based on distance were designed. The benchmark datasets in UCI Learning Repository were used to verify the performance of the algorithms, the feasibility of the granular computing classification algorithms was verified by the experimental results

     

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