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Mixed intelligent-multivariate missing imputation

Autor
Gibert, Karina
Tipus d'activitat
Article en revista
Revista
International journal of computer mathematics
Data de publicació
2014-01-02
Volum
91
Número
1
Pàgina inicial
85
Pàgina final
96
DOI
https://doi.org/10.1080/00207160.2013.783209 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/28477 Obrir en finestra nova
URL
http://www.tandfonline.com/doi/abs/10.1080/00207160.2013.783209#.U3NIvCj66jE Obrir en finestra nova
Resum
In real applications, important rates of missing data are often found and have to be pre-processed before the analysis. The literature for missing imputation is abundant. However, the most precise imputation methods require long time, and sometimes speci c software; this implies a signi cant delay to get nal results. The Mixed Intelligent-Multivariate Missing Im- putation (MIMMI) method is proposed as a hybrid missing imputation methodology based on clustering. MIMMI is a non parametric method...
Citació
Gibert, Karina. Mixed intelligent-multivariate missing imputation. "International journal of computer mathematics", 02 Gener 2014, vol. 91, núm. 1, p. 85-96.
Paraules clau
BORDERLINE PERSONALITY-DISORDER, HEALTH SYSTEMS, clustering, multivariate imputation, prior expert knowledge
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
KEMLG - Grup d´Enginyeria del Coneixement i Aprenentatge Automàtic

Participants