Nonparametric Hyperbox Granular Computing Classification Algorithms

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 classificat...

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Bibliographic Details
Published in:Information (Basel) 2019-02, Vol.10 (2), p.76
Main Authors: Liu, Hongbing, Diao, Xiaoyu, Guo, Huaping
Format: Article
Language:English
Subjects:
Algorithms
Classification
Computation
Data analysis
distance
granular computing
Granular materials
hyperbox granule
join operation
Mathematical analysis
Nonparametric statistics
Teaching methods
Vectors (mathematics)
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Summary: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.
ISSN:2078-2489
2078-2489
DOI:10.3390/info10020076