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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Feature clustering based support vector machine recursive feature elimination for gene selection
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Feature clustering based support vector machine recursive feature elimination for gene selection

机译：基于特征聚类支持向量机递归功能消除基因选择

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摘要

In a DNA microarray dataset, gene expression data often has a huge number of features(which are referred to as genes) versus a small size of samples. With the development of DNA microarray technology, the number of dimensions increases even faster than before, which could lead to the problem of the curse of dimensionality. To get good classification performance, it is necessary to preprocess the gene expression data. Support vector machine recursive feature elimination (SVM-RFE) is a classical method for gene selection. However, SVM-RFE suffers from high computational complexity. To remedy it, this paper enhances SVM-RFE for gene selection by incorporating feature clustering, called feature clustering SVM-RFE (FCSVM-RFE). The proposed method first performs gene selection roughly and then ranks the selected genes. First, a clustering algorithm is used to cluster genes into gene groups, in each which genes have similar expression profile. Then, a representative gene is found to represent a gene group. By doing so, we can obtain a representative gene set. Then, SVM-RFE is applied to rank these representative genes. FCSVM-RFE can reduce the computational complexity and the redundancy among genes. Experiments on seven public gene expression datasets show that FCSVM-RFE can achieve a better classification performance and lower computational complexity when compared with the state-the-art-of methods, such as SVM-RFE.
机译：在DNA微阵列数据集中，基因表达数据通常具有大量的特征（称为基因）与小尺寸的样本相比。随着DNA微阵列技术的发展，尺寸的数量比以前的速度增加，这可能导致维度诅咒的问题。为了获得良好的分类性能，有必要预处理基因表达数据。支持向量机递归功能消除（SVM-RFE）是基因选择的经典方法。然而，SVM-RFE具有高计算复杂性。为了解决它，本文通过结合特征群集，称为特征聚类SVM-RFE（FCSVM-RFE）来增强SVM-RFE的基因选择。所提出的方法首先大致进行基因选择，然后对所选基因进行排序。首先，将聚类算法用于将基因进行聚类为基因组，在每个基因具有相似的表达谱中。然后，发现代表性基因代表基因组。通过这样做，我们可以获得代表性基因集。然后，施用SVM-RFE对这些代表性基因进行排名。 FCSVM-RFE可以降低基因之间的计算复杂性和冗余。七个公共基因表达数据集的实验表明，与诸如SVM-RFE等最新方法相比，FCSVM-RFE可以实现更好的分类性能和较低的计算复杂性。

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