Computer Science ›› 2015, Vol. 42 ›› Issue (7): 285-290.doi: 10.11896/j.issn.1002-137X.2015.07.061

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Group Feature Selection Algorithm for Data Sets with Missing Data

WANG Feng WEI Wei   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Many real data increase dynamically in size.With the rapid development of data processing tools,new data are usually increased in groups.In this paper,based on rough set theory,a group rough feature selection algorithm was proposed to deal with dynamic data sets with missing data.Firstly,the group incremental mechanism of information entropy was analyzed,and then significance of feature was defined based on the mechanism.On this basis,a group feature selection algorithm was constructed,which can be used to deal with dynamic data sets with missing data effectively.Experimental results show that the new algorithm is feasible and efficient.

Key words: Dynamic data sets,Missing data,Information entropy,Group feature selection

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