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Outliers detection based on negative selection algorithm

Authors: null Zhang Xiaoling; null Wen Jun; null Wan XiaoFeng;

Outliers detection based on negative selection algorithm

Abstract

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.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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