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Information 2019, 10(2), 76; https://doi.org/10.3390/info10020076

Nonparametric Hyperbox Granular Computing Classification Algorithms

1
Center of Computing, Xinyang Normal University, Xinyang 464000, China
2
School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
*
Author to whom correspondence should be addressed.
Received: 8 January 2019 / Revised: 1 February 2019 / Accepted: 15 February 2019 / Published: 24 February 2019
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Abstract

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. View Full-Text
Keywords: hyperbox granule; granular computing; distance; join operation hyperbox granule; granular computing; distance; join operation
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Liu, H.; Diao, X.; Guo, H. Nonparametric Hyperbox Granular Computing Classification Algorithms. Information 2019, 10, 76.

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