文章基本信息

标题：Nonparametric Hyperbox Granular Computing Classification Algorithms
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作者：Hongbing Liu ; Xiaoyu Diao ; Huaping Guo 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2019
卷号：10
期号：2
页码：76-89
DOI：10.3390/info10020076
出版社：MDPI Publishing
摘要：Parametric granular computing classification algorithms lead to difficulties in terms of parameter selection, the multiple performance times of algorithms, and increased algorithm complexity in comparison with nonparametric algorithms. We present nonparametric hyperbox granular computing classification algorithms (NPHBGrCs). Firstly, the granule has a hyperbox form, with the beginning point and the endpoint induced by any two vectors in N-dimensional (N-D) space. Secondly, the novel distance between the atomic hyperbox and the hyperbox granule is defined to determine the joining process between the atomic hyperbox and the hyperbox. Thirdly, classification problems are used to verify the designed NPHBGrC. The feasibility and superiority of NPHBGrC are demonstrated by the benchmark datasets compared with parametric algorithms such as HBGrC.
关键词：hyperbox granule; granular computing; distance; join operation hyperbox granule ; granular computing ; distance ; join operation