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Heuristic filter feature selection methods for medical datasets

机译：Heuuristic过滤器功能选择医疗数据集的选择方法

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摘要

Gene selection is the process of selecting the optimal feature subset in an arbitrary dataset. The significance of gene selection is in high dimensional datasets in which the number of samples and features are low and high respectively. The major goals of gene selection are increasing the accuracy, finding the minimal effective feature subset, and increasing the performance of evaluations. This paper proposed two heuristic methods for gene selection, namely, Xvariance against Mutual Congestion. Xvariance tries to classify labels using internal attributes of features however Mutual Congestion is frequency based. The proposed methods have been conducted on eight binary medical datasets. Results reveal that Xvariance works well with standard datasets, however Mutual Congestion improves the accuracy of high dimensional datasets considerably.
机译：基因选择是在任意数据集中选择最佳特征子集的过程。基因选择的意义在于高维数据集，其中样品和特征的数量分别低且高。基因选择的主要目标正在增加精度，找到最小的有效特征子集，并提高评估的性能。本文提出了两种对基因选择的启发式方法，即Xvariance对相互拥塞。 xvariance尝试使用特征的内部属性对标签进行分类，但相互拥塞是基于频率的。已经在八个二进制医学数据集中进行了所提出的方法。结果表明，Xvariance符合标准数据集的适用，但相互拥塞可以显着提高高维数据集的准确性。

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