Tournament screening cum EBIC for feature selection with high-dimensional feature spaces
Tournament screening cum EBIC for feature selection with high-dimensional feature spaces
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DOI：
10.1007/s11425-009-0089-4
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发表时间：
2009-06-01
期刊：
影响因子：
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通讯作者：
Chen JiaHua
中科院分区：
文献类型：
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作者：
Chen Zehua;Chen JiaHua
以相对较小的样本量和极高维度的特征空间为特征的特征选择在当代统计学的许多领域都很常见。特征空间的高维性导致了严重的困难：(1)即使特征是随机独立的，特征之间的样本相关性也会变得很高；（ii）计算变得难以处理。这些困难使得传统方法要么不适用，要么效率低下。对特征空间进行降维处理后再采用低维方法是解决该问题的唯一可行方法。沿着这条路线，我们在本文中开发了一种锦标赛筛选和EBIC方法，用于高维特征空间的特征选择。比赛筛选的程序模仿了比赛的程序。从理论上证明了竞赛筛选具有确定的筛选性质，这是任何有效筛选程序都应满足的必要性质。数值研究表明，比赛筛选与EBIC方法相比，具有较高的阳性选择率和较低的错误发现率。
The feature selection characterized by relatively small sample size and extremely high-dimensional feature space is common in many areas of contemporary statistics. The high dimensionality of the feature space causes serious difficulties: (i) the sample correlations between features become high even if the features are stochastically independent; (ii) the computation becomes intractable. These difficulties make conventional approaches either inapplicable or inefficient. The reduction of dimensionality of the feature space followed by low dimensional approaches appears the only feasible way to tackle the problem. Along this line, we develop in this article a tournament screening cum EBIC approach for feature selection with high dimensional feature space. The procedure of tournament screening mimics that of a tournament. It is shown theoretically that the tournament screening has the sure screening property, a necessary property which should be satisfied by any valid screening procedure. It is demonstrated by numerical studies that the tournament screening cum EBIC approach enjoys desirable properties such as having higher positive selection rate and lower false discovery rate than other approaches.