Impact of Feature Selection on Support Vector Machine Using Microarray Gene Expression Data

C. Wahid, A. Ali, K. Tickle
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引用次数: 3

Abstract

Recent researches have investigated the impact of feature selection methods on the performance of support vector machine (SVM) and claimed that no feature selection methods improve it in high dimension. However, they have based this argument on their experiments with simulated data. We have taken this claim as a research issue and investigated different feature selection methods on the real time micro array gene expression data. Our research outcome indicates that feature selection methods do have a positive impact on the performance of SVM in classifying micro array gene expression data.
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基于微阵列基因表达数据的特征选择对支持向量机的影响
最近的研究研究了特征选择方法对支持向量机(SVM)性能的影响,认为没有一种特征选择方法能在高维情况下改善支持向量机(SVM)的性能。然而,他们的论点是基于他们的模拟数据实验。我们将这一主张作为研究课题,对实时微阵列基因表达数据的不同特征选择方法进行了研究。我们的研究结果表明,特征选择方法确实对支持向量机在微阵列基因表达数据分类中的性能有积极的影响。
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