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一种面向非平衡生物医学数据的自训练半监督方法

A Self-training Semi-supervised Method for imbalanced Biomedical Data Sets
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摘要 生物医学复杂关系提取已经成为文本挖掘领域的焦点,而用于训练预测模型的注释语料库高度非平衡,且大多是针对单个注释语料库训练的监督模型,极大地限制了系统性能。因此,提出一种显著空间SVM自训练半监督学习方法,通过平衡初始模型诱导未标签训练数据,将领域知识纳入事件提取模型,识别多数类子集,构建预测模型。通过设计实验评估证明了训练语料库的有效性。
作者 王凯 杨枢 张钰
机构地区 蚌埠医学院卫生管理系 合肥工业大学计算机科学与信息学院
出处 《大庆师范学院学报》 2017年第6期75-79,共5页 Journal of Daqing Normal University
基金 安徽省高校自然科学研究项目(KJ2015B023by)
关键词 自训练 半监督 非平衡数据 生物医学领域
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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大庆师范学院学报
2017年 第6期

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