Next Article in Journal
Predictable Trajectory Planning of Industrial Robots with Constraints
Previous Article in Journal
An Interdisciplinary Approach to the Nanomanipulation of SiO2 Nanoparticles: Design, Fabrication and Feasibility
Open AccessArticle

Neighborhood Granule Classifiers

by Hongbo Jiang 1 and Yumin Chen 2,*
1
School of Economics and Management, Xiamen University of Technology, Xiamen 361024, China
2
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; https://doi.org/10.3390/app8122646
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
Show Figures

Figure 1

MDPI and ACS Style

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

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop