Binary Grid Domain Description Based on Morphology for One-class Classification

doi:10.3969/j.issn.1006-2475.2015.03.012

Abstract

Abstract: A one-class classifier of Binary Grid Domain Description (BGDD) based on morphology is proposed for solving unbalanced samples classification problems. In this method, the sample space is first divided into grids. Then, an approximate domain description can be obtained by putting samples into these grids. These grids are divided into object grids and background grids, the grid which contains at least one sample is defined as the object grid, while the grid without any sample is defined as the background grid. Next, morphological closing and opening operations are applied to object grids to obtain the domain description of the training samples. Experiments based on both artificial and real-world datasets were done and comparative experimental results were present. Experimental results show that the BGDD classifier is an effective classification method for high classification accuracy and fast training speed.
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Key words: morphological operation, grid, one-class classification, domain description

CLC Number:

TP181

GAO Feng, QU Jian-ling, GUO Chao-ran, SUN Wen-zhu. Binary Grid Domain Description Based on Morphology for One-class Classification[J]. Computer and Modernization, 2015, 0(3): 57-61.

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