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Neighborhood Granule Classifiers

by 1 and 2,*
School of Economics and Management, Xiamen University of Technology, Xiamen 361024, China
School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(12), 2646;
Received: 11 October 2018 / Revised: 11 December 2018 / Accepted: 11 December 2018 / Published: 17 December 2018
Classifiers are divided into linear and nonlinear classifiers. The linear classifiers are built on a basis of some hyper planes. The nonlinear classifiers are mainly neural networks. In this paper, we propose a novel neighborhood granule classifier based on a concept of granular structure and neighborhood granules of datasets. By introducing a neighborhood rough set model, the condition features and decision features of classification systems are respectively granulated to form some condition neighborhood granules and decision neighborhood granules. These neighborhood granules are sets; thus, their calculations are intersection and union operations of sets. A condition neighborhood granule and a decision neighborhood granule form a granular rule, and the collection of granular rules constitutes a granular rule library. Furthermore, we propose two kinds of distance and similarity metrics to measure granules, which are used for the searching and matching of granules. Thus, we design a granule classifier by the similarity metric. Finally, we use the granule classifier proposed in this paper for a classification test with UCI datasets. The theoretical analysis and experiments show that the proposed granule classifier achieves a better classification performance under an appropriate neighborhood granulation parameter. View Full-Text
Keywords: neighborhood granule classifiers; neighborhood rough sets; granular structures; granular distances; granular computing neighborhood granule classifiers; neighborhood rough sets; granular structures; granular distances; granular computing
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MDPI and ACS Style

Jiang, H.; Chen, Y. Neighborhood Granule Classifiers. Appl. Sci. 2018, 8, 2646.

AMA Style

Jiang H, Chen Y. Neighborhood Granule Classifiers. Applied Sciences. 2018; 8(12):2646.

Chicago/Turabian Style

Jiang, Hongbo, and Yumin Chen. 2018. "Neighborhood Granule Classifiers" Applied Sciences 8, no. 12: 2646.

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