基于相互近邻法的基因选择

H. Shashirekha, A. Wani
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引用次数: 1

摘要

基因表达数据由于存在数千个基因(特征)但样本数量很少而遭受维度的诅咒。特征选择算法解决了大特征空间的问题,该算法旨在通过去除冗余和不相关的特征来找到相对较小的重要特征集,从而提高性能(例如,更高的分类精度),降低计算成本,提高模型的可解释性并更好地理解结果。在本文中,我们探索了互近邻(MNN)和均值检验方法的可能应用,从高维基因表达数据中选择重要基因,并将其性能与其他三种已知算法进行比较。利用kNN分类器对这些算法的性能进行了测试,并给出了测试结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gene selection by Mutual Nearest Neighbor approach
Gene expression data suffer from the curse of dimensionality due to the presence of several thousands of genes (features) but a small number of samples. This problem of large feature space is addressed by feature selection algorithms which aim at finding a comparatively small set of significant features by removing the redundant and irrelevant features thereby increasing the performance (e.g., higher accuracy for classification), decreasing the computational cost and improving the model interpretability and comprehending the results in an better way. In this paper, we explore the possible application of Mutual Nearest Neighbor (MNN) and Mean Test approaches to select significant genes from high dimensional gene expression data and compare their performances with three other well known algorithms. kNN classifier is used to measure the performances of these algorithms and the results are illustrated.
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