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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >A Top-r Feature Selection Algorithm for Microarray Gene Expression Data
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A Top-r Feature Selection Algorithm for Microarray Gene Expression Data

机译：芯片基因表达数据的Top-r特征选择算法

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摘要

Most of the conventional feature selection algorithms have a drawback whereby a weakly ranked gene that could perform well in terms of classification accuracy with an appropriate subset of genes will be left out of the selection. Considering this shortcoming, we propose a feature selection algorithm in gene expression data analysis of sample classifications. The proposed algorithm first divides genes into subsets, the sizes of which are relatively small (roughly of size h), then selects informative smaller subsets of genes (of size r < h) from a subset and merges the chosen genes with another gene subset (of size r) to update the gene subset. We repeat this process until all subsets are merged into one informative subset. We illustrate the effectiveness of the proposed algorithm by analyzing three distinct gene expression data sets. Our method shows promising classification accuracy for all the test data sets. We also show the relevance of the selected genes in terms of their biological functions.
机译：大多数常规特征选择算法都具有一个缺点，即在分类准确度方面可以表现良好的弱基因，而适当的基因子集则会被排除在选择之外。考虑到这一缺点，我们提出了一种在样本分类的基因表达数据分析中的特征选择算法。提出的算法首先将基因分成子集，子集的大小相对较小（大约为h），然后从一个子集中选择信息量较小的子集（r

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