基于随机森林相似度矩阵差异性的特征选择  

Feature selection of random forest-based proximity matrix difference

作　　者：周绮凤[1] 洪文财[1] 杨帆[1] 罗林开[1] 

机构地区：[1]厦门大学自动化系,福建厦门361005

出　　处：《华中科技大学学报（自然科学版）》2010年第4期58-61,共4页Journal of Huazhong University of Science and Technology(Natural Science Edition)

基　　金：福建省自然科学基金资助项目(2009J05153)

摘　　要：将随机森林的相似度矩阵看做一种特殊的核度量,利用该度量对模型参数的鲁棒性和特征变化的敏感性,提出一种特征选择的方法.采用相似度矩阵,计算训练样本类内和类间相似性比率.再利用特征值随机置换技术,将相似性比率的变化量作为特征重要性度量指标,从而对所有特征进行排序.试验结果表明,该方法能充分利用全部样本的信息,有效地进行特征选择,且其性能优于基于袋外数据误差率估计的特征选择方法.A feature selection method is proposed,after analyzing proximity matrix＇s to random forest model and its sensitiveness to the variation of features.Proximity matrix is taken as a special kernel measurement to compute the proximity ratio between inner-class and the inter-class,then permutes the values of feature randomly and the difference of proximity ratio was takes as the assessment criterion for feature importance.The process yields a ranking for all features.Experimental results show that the method achieves good effects and performs better than that of the method based on out-of-bag （OOB） error rate.

关 键 词：特征选择 度量 差异性 相似度矩阵 随机森林 随机置换 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]