基于统计特性随机森林算法的特征选择  

Feature selection based on statistical random forest algorithm

作　　者：宋源[1] 梁雪春[1] 张然[1] 

机构地区：[1]南京工业大学自动化与电气工程学院,南京211816

出　　处：《计算机应用》2015年第5期1459-1461,1466,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(51205185);江苏省普通高校研究生科研创新计划项目(KYLX_0754)

摘　　要：针对由静息态功能磁共振成像(R-f MRI)得到的脑功能连接矩阵数据运用传统特征选择方法处理的结果,存在特征冗余,无法确定最终特征维数等问题,提出一种全新的特征选择算法。该算法在随机森林(RF)算法中结合统计特性,根据袋外数据的分类效果得到保留的特征,并将其运用在对精神分裂患者与正常被试者的识别实验中。实验结果表明,与传统的主成分分析(PCA)方法相比,该算法可以有效保留重要特征,提高识别精度,且保留的特征具有很好的医学解释性。Focused on the traditional methods of feature selection for brain functional connectivity matrix derived from Resting-state functional Magnetic Resonance Imaging（ R-f MRI） have feature redundancy, cannot determine the final feature dimension and other problems, a new feature selection algorithm was proposed. The algorithm combined Random Forest（ RF）algorithm in statistical method, and applied it in the identification experiment of schizophrenic and normal patients, according to the features are obtained by the classification results of out of bag data. The experimental results show that compared to the traditional Principal Component Analysis（ PCA）, the proposed algorithm can effectively retain important features to improve recognition accuracy, which have good medical explanation.

关 键 词：随机森林 统计特性 静息态功能磁共振成像 脑功能连接矩阵 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]