Title :
Using Entropy to Impute Missing Data in a Classification Task
Author :
Delavallade, Thomas ; Dang, Thanh Ha
Author_Institution :
Univ. Pierre et Marie Curie - Paris6, Paris
Abstract :
In real applications, part of the data is usually missing. But most techniques of data analysis and data mining can only deal with complete data. In this paper, a new taxonomy of imputation methods is proposed. Within this taxonomy a new technique, based on entropy measures is introduced. Its behaviour is studied through an empirical comparative analysis.
Keywords :
data analysis; data mining; entropy; pattern classification; data analysis; data classification; data mining; entropy measure; imputation method; Data analysis; Data mining; Databases; Decision trees; Entropy; Machine learning; Machine learning algorithms; Predictive models; Statistics; Taxonomy;
Conference_Titel :
Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International
Conference_Location :
London
Print_ISBN :
1-4244-1209-9
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2007.4295430
