Improving the performance of SVM-RFE to select genes in microarray data.
Improving the performance of SVM-RFE to select genes in microarray data.
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DOI：
10.1186/1471-2105-7-s2-s12
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发表时间：
2006-09-06
影响因子：
3
通讯作者：
Wilkins D
中科院分区：
文献类型：
--
作者：
Ding Y;Wilkins D
递归特征消除是一种常见的、经过充分研究的方法，用于减少用于进一步分析或开发预测模型的属性数量。RFE算法的有效性通常被认为是极好的，但使用它的主要障碍是所需的计算能力。这里我们介绍一种采用模拟退火法思想的RFE的变种。该算法的目标是在尽可能不影响约简特征集质量的情况下，通过一次消除特征块来提高递归特征消除的计算性能。该算法已经在几个大型基因表达数据集上进行了测试。RFE算法使用支持向量机来辅助识别最不有用的基因(S)来消除。该算法简单高效，生成的属性集与RFE生成的属性集非常相似。
Recursive Feature Elimination is a common and well-studied method for reducing the number of attributes used for further analysis or development of prediction models. The effectiveness of the RFE algorithm is generally considered excellent, but the primary obstacle in using it is the amount of computational power required. Here we introduce a variant of RFE which employs ideas from simulated annealing. The goal of the algorithm is to improve the computational performance of recursive feature elimination by eliminating chunks of features at a time with as little effect on the quality of the reduced feature set as possible. The algorithm has been tested on several large gene expression data sets. The RFE algorithm is implemented using a Support Vector Machine to assist in identifying the least useful gene(s) to eliminate. The algorithm is simple and efficient and generates a set of attributes that is very similar to the set produced by RFE.