%0 Journal Article
%T 基于决策树桩的元特征提取<br>The Extraction of Meta-Feature Based on Decision Stump
%A 曾子林
%A 陈建军
%J -
%D 2018
%R 10.16357/j.cnki.issn1000-5862.2018.06.12
%X “No Free Lunch”定理表明:若无任何先验假设,则没有理由认为一种算法优于另一种算法.算法的性能与问题的元特征密切相关.目前的元特征提取方法只关注从数据集中提取元特征,而忽略了候选算法元特征的提取.为此,在原有元特征集合的基础上提出基于决策树桩的元特征提取方法,将候选算法信息纳入新的元特征集合中.实验表明:在传统元特征集合中加入基于决策树桩的元特征后,算法排序的预测准确率能够得到显著提高.<br>The "No Free Lunch" theorem shows that there is no reason to think that one algorithm is superior to the other one without any prior assumptions.The performance of algorithm is closely related to the meta-feature of problem.The current meta-feature extraction method is only concerned with extracting meta-feature from the data set,while ignoring the meta-feature extraction of candidate algorithms.Therefore,an extraction method based on decision stump is proposed,which can effectively reflect the information of candidate algorithms.Experiments show that the new meta-feature sets significantly increase the prediction accuracy of algorithm ranking
%K 元特征
%K 算法性能
%K 算法排序
%K 决策树桩<br>元特征 算法性能 算法排序 决策树桩
%K 元特征 算法性能 算法排序 决策树桩
%K 元特征 算法性能 算法排序 决策树桩
%K 元特征 算法性能 算法排序 决策树桩
%U http://lkxb.jxnu.edu.cn//oa/darticle.aspx?type=view&id=201806012