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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

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基于随机森林相似度矩阵差异性的特征选择.pdf (217.6Kb)
Date
2010
Author
周绮凤
洪文财
杨帆
罗林开
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  • 航空航天-已发表论文 [2242]
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Abstract
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.
 
Citation
华中科技大学学报(自然科学版),2010,(4):63-66
URI
https://dspace.xmu.edu.cn/handle/2288/105274

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