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一致群稳定特征选择

Consensus Group Stable Feature Selection
课程网址: http://videolectures.net/kdd09_loscalzo_cgsfs/  
主讲教师: Steven Loscalzo
开课单位: 纽约州立大学
开课时间: 2009-09-14
课程语种: 英语
中文简介:
在高维和小样本数据的特征选择中,稳定性是一个重要但尚未解决的问题。在本文中,我们表明特征选择的稳定性强烈依赖于样本大小。我们提出了一种新的稳定特征选择框架,该框架首先从训练样本的子采样中识别共识特征组,然后通过将每个共识特征组视为单个实体来执行特征选择。在合成和现实世界数据集上的实验表明,在该框架下开发的算法在减轻小样本大小的问题方面是有效的,并且导致比现有技术特征选择算法更稳定的特征选择结果和相当或更好的泛化性能。
课程简介: Stability is an important yet under-addressed issue in feature selection from high-dimensional and small sample data. In this paper, we show that stability of feature selection has a strong dependency on sample size. We propose a novel framework for stable feature selection which first identifies consensus feature groups from subsampling of training samples, and then performs feature selection by treating each consensus feature group as a single entity. Experiments on both synthetic and real-world data sets show that an algorithm developed under this framework is effective at alleviating the problem of small sample size and leads to more stable feature selection results and comparable or better generalization performance than state-of-the-art feature selection algorithms. 
关 键 词: 稳定特征; 训练样本; 数据集
课程来源: 视频讲座网
最后编审: 2021-12-22:liyy
阅读次数: 58