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Outliers detection based on negative selection algorithm

机译:基于负选择算法的离群值检测

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This paper presents a new approach to detect outliers. This paper detailedly introduces how to apply negative selection algorithm in outliers detection. Firstly, the maximum distance among all points is divided into a certain number of ranges which are encoded to binary codes. And then the distances between each point and a certain number (for example 20) points nearby are encoded to binary string based on the binary codes which were presented. Negative selection algorithm based on binary strings is applied to detect outliers. Experiments on random data to evaluate the effectiveness of the approach are presented. Experiments show that this approach can detect outliers effectively.
机译:本文提出了一种检测异常值的新方法。本文详细介绍了负选择算法在离群值检测中的应用。首先,将所有点之间的最大距离划分为一定数量的范围,这些范围被编码为二进制代码。然后,根据给出的二进制代码,将每个点与附近一定数量(例如20个)点之间的距离编码为二进制字符串。将基于二进制字符串的负选择算法应用于离群值检测。提出了关于随机数据的实验,以评估该方法的有效性。实验表明,该方法可以有效地检测离群值。

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