计算机应用研究2017，Vol.34Issue(12)：3564-3568,5.DOI:10.3969/j.issn.1001-3695.2017.12.010
面向主动学习的模糊核聚类采样算法
Sampling algorithm using kernel-based fuzzy clustering for active learning
摘要
Abstract
Since it is difficult to select representative samples for active learning when constructing the initial classifier,this paper proposed a sampling algorithm using kernel-based fuzzy clustering.This algorithm began with dividing the sample set via clustering analysis technology,then it extracted samples from regions near the center and the boundary of clusters respectively and labeled them.And in the final phase it constructed the initial classifier using these labeled samples.In this algorithm,it transformed the point in the original sample space into a high dimensional feature space by Gaussian kernel function with the aim of linear clustering,and it introduced an initial cluster center selection method based on local density to improve its cluster performance.In order to ameliorate its sampling quality,this paper designed a sampling proportion allocation strategy utilizing the number of samples of divided each cluster.At the end of sampling,it used a fallback sampling strategy to ensure that the number of samples was up to the standard.The experimental results have demonstrated that the proposed algorithm can effectively reduce the cost of labeling samples when constructing the initial classifier,and get a higher classification accuracy.关键词
高斯核函数/聚类分析/采样/主动学习/分类Key words
Gaussian kernel function/clustering analysis/sampling/active learning/classification分类
信息技术与安全科学引用本文复制引用
王勇臻,陈燕,张金松..面向主动学习的模糊核聚类采样算法[J].计算机应用研究,2017,34(12):3564-3568,5.基金项目
国家自然科学基金资助项目(71271034) （71271034）
辽宁省自然科学基金资助项目(2014025015) （2014025015）
青年骨干教师基金资助项目(3132016045) （3132016045）
