Feature Selection by Iterative Block Addition and Block Deletion
Feature Selection by Iterative Block Addition and Block Deletion
复制标题
通过迭代块添加和块删除进行特征选择
DOI：
10.1109/smc.2013.456
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Shigeo Abe
中科院分区：
文献类型：
--
作者：
大西 慶秀;大屋 英稔;中野 和司;Shigeo Abe
在我们以前的工作中，我们提出了块添加（BA）和块删除（BD）的特征选择。在本文中，为了进一步减少特征，我们重复BABD直到没有特征被消除。在我们的方法中，我们一次向特征集中添加几个特征，直到满足停止条件。然后，我们删除功能，不恶化的选择标准的块删除。我们为选定的特征集添加块和删除块，直到没有特征被删除。通过对微阵列数据集的计算机实验表明，对于某些微阵列数据集，通过迭代BABD进一步删除特征，以识别错误率与边缘误差平均值的加权和作为选择和排序标准，在获得具有高泛化能力的特征集方面优于识别错误率。
In our previous work, we proposed feature selection by block addition (BA) and block deletion (BD). In this paper, to further reduce features, we iterate BABD until no features are eliminated. In our method, we add several features at a time to the feature set until a stopping condition is satisfied. Then we delete features that do not deteriorate the selection criterion by block deletion. We iterate block addition and block deletion for the selected feature set until no features are eliminated. By computer experiments using micro array data sets we show that for some micro array data sets, features are further deleted by iterating BABD and as the selection and ranking criteria the weighted sum of the recognition error rate and the average of margin errors is better than the recognition error rate in obtaining a feature set with high generalization ability.