A discernibility-based approach to feature selection for microarray data

Z. Voulgaris, G. Magoulas
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引用次数: 3

Abstract

Feature selection has been used widely for a variety of data, yielding higher speeds and reduced computational cost for the classification process. However, it is in microarray datasets where its advantages become more evident and are more required. In this paper we present a novel approach to accomplish this based on the concept of discernibility that we introduce to depict how separated the classes of a dataset are. We develop and test two independent feature selection methods that follow this approach. The results of our experiments on four microarray datasets show that discernibility-based feature selection reduces the dimensionality of the datasets involved without compromising the performance of the classifiers.
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微阵列数据特征选择的可辨别性方法
特征选择已广泛用于各种数据,为分类过程提供了更高的速度和更低的计算成本。然而,它是在微阵列数据集,其优势变得更加明显,更需要。在本文中,我们提出了一种新的方法来实现这一目标,该方法基于我们引入的可辨性概念来描述数据集的类是如何分离的。我们开发并测试了遵循这种方法的两种独立的特征选择方法。我们在四个微阵列数据集上的实验结果表明,基于可别性的特征选择在不影响分类器性能的情况下降低了所涉及数据集的维数。
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