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A new method for filling missing values by gray relational analysis

Authors: null Bingwei Han; null Shuangjiu Xiao; null Lu Liu; null Zhijing Wu;

A new method for filling missing values by gray relational analysis

Abstract

In Data Mining and Machine Learning, the missing attribute will have a negative impact on the learning results. The filling of missing values is a very challenging work. In this paper, a new algorithm based on gray relational analysis is presented, which takes the differences of the relationships between the properties into account. When calculating the gray relational grade, the weights of attributes will be considered. The experimental results demonstrate that this method performs well when filling the discrete missing values.

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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!
1
Average
Average
Average
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