Tree-based procedures have been recently considered as non parametric tools for missing data imputation when dealing with large data structures and no probability assumption. A previous work used incremental algorithm based on cross-validated decision trees and a lexicographic ordering of the single data to be imputed. This paper considers an ensemble method where tree-based model is used as learner. Furthermore, the proposed method allows more accurate imputations through a more efficient algorithm. A simulation case study shows the overall good performance of the proposed method against some competitors. A MatLab implementation enriches Tree Harvest Software for non-standard classification and regression trees.

Boosted Incremental Tree-based Imputation of Missing Data

SICILIANO, ROBERTA;ARIA, MASSIMO;D'AMBROSIO, ANTONIO
2006

Abstract

Tree-based procedures have been recently considered as non parametric tools for missing data imputation when dealing with large data structures and no probability assumption. A previous work used incremental algorithm based on cross-validated decision trees and a lexicographic ordering of the single data to be imputed. This paper considers an ensemble method where tree-based model is used as learner. Furthermore, the proposed method allows more accurate imputations through a more efficient algorithm. A simulation case study shows the overall good performance of the proposed method against some competitors. A MatLab implementation enriches Tree Harvest Software for non-standard classification and regression trees.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11588/202831
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact