摘要
为提高半监督分类的性能,提出一种安全的基于分歧的半监督分类算法Safe Co-SSC。通过有标记样本训练3个有监督分类器,利用无标记样本的信息增加分类器的差异性,采取3个分类器加权投票的策略实现对无标记样本的伪标记;对伪标记样本进行二次验证,选用能使分类器误差减小的新增标记样本扩充标记样本集。保证新样本的添加既减小了分类器的分类误差,又提高了分类器的分歧性。对UCI数据集进行分类实验的结果表明,该算法具有较高的分类率和样本标记率。
In order to improve the performance of semi-supervised classifier , a safe disagreement-based semi-supervised classifica-tion algorithm named Safe Co-SSC was proposed .The limited labeled samples were divided into three equal training sets and used to train three classifiers by a supervised learning algorithm .A large number of unlabeled samples were used to increase the differences be-tween the classifiers and the weighted voting strategy was used to achieve pseudo -labeled for unlabeled samples .Passing through sec-ondary verification, the ones making classifier error minimum were added into the labeled samples set .Finally, the experiment was car-ried out on the UCI data set , the results showed that the proposed algorithm had higher classification rate and sample labeling rate .
出处
《西华大学学报（自然科学版）》
CAS
2014年第5期1-6,共6页
Journal of Xihua University:Natural Science Edition
基金
陕西省教育厅科研计划项目(12JK0748)
商洛学院科研基金(10sky1001)
关键词
半监督学习
分类
安全性
分歧
semi-supervised learning
classification
safety
disagreement
