A Novel Semi-feature selection method based on hybrid feature selection mechanism

摘要：

Many Semi-supervised learning applications require a feature selection method to deal with the unlabeled samples. Traditional researches deal it either with the “filter-type feature selection mechanism, which may not work well for classification tasks or “wrapper mechanism, which need high computational cost Here we proposed a new semi-supervised feature selection method based on hybrid feature selection mechanism. Its principle lies in using Relief Wrapper method to explore the usage of unlabeled examples, which will help for training classifiers. In essence, it uses unlabeled examples to extend the initial labeled training set with the help of classifiers. Extensive experiments on publicly available datasets and formal analysis show its nice combination of efficiency and accuracy.

关键词： Semi-supenrised learning Relief Wrapper Semi-feature selection

作者: Shangzhi Zheng Hualong Bu

作者单位: Department of Computer Science and Technology, Chaohu University Chaohu China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 590-593

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）