Abstract
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.
Highlights
We focus on the neighborhood granulation of classification systems and propose a granular structure, granular distance, and granular rule by a neighborhood granulation, so as to build a new granule classifier model
It can be seen that Neighborhood Granule Classifier was based on Relative (NGCR) and Neighborhood Granule Classifier on Absolute (NGCA) were better than the traditional K-Nearest Neighbor (KNN) under suitable neighborhood parameter values
By the neighborhood granulation of samples, a new granule classifier is proposed, which is constructed on the operations of sets
Summary
Classification methods mainly focus on statistical analysis [6,7], neural networks [8,9,10], rule reasoning [11,12], etc. We propose a new classifier model based on the set theory and the neighborhood information granulation. We focus on the neighborhood granulation of classification systems and propose a granular structure, granular distance, and granular rule by a neighborhood granulation, so as to build a new granule classifier model. Theoretical analysis and experimental results show that the proposed classifier can achieve a better classification performance under a suitable granulating parameter.
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