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Good methods for coping with missing data in decision trees

Twala, B. E. T. H.; Jones, M. C. and Hand, D. J. (2008). Good methods for coping with missing data in decision trees. Pattern Recognition Letters, 29(7) pp. 950–956.

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DOI (Digital Object Identifier) Link: http://doi.org/10.1016/j.patrec.2008.01.010
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Abstract

We propose a simple and effective method for dealing with missing data in decision trees used for classification. We call this approach 'missingness incorporated in attributes' (MIA). It is very closely related to the technique of treating 'missing' as a category in its own right, generalizing it for use with continuous as well as categorical variables. We show through a substantial data-based study of classification accuracy that MIA exhibits consistently good performance across a broad range of data types and of sources and amounts of missingness. It is competitive with the best of the rest (particularly, a multiple imputation EM algorithm method; EMMI) while being conceptually and computationally simpler. A simple combination of MIA and EMMI is slower but even more accurate.

Item Type: Journal Article
Copyright Holders: 2008 Elsevier B.V.
ISSN: 0167-8655
Keywords: C4.5; CART; EM algorithm; fractional cases; missingness as attribute; multiple imputation
Academic Unit/Department: Mathematics, Computing and Technology > Mathematics and Statistics
Mathematics, Computing and Technology
Item ID: 22531
Depositing User: Sarah Frain
Date Deposited: 18 Aug 2010 10:32
Last Modified: 23 Feb 2016 17:32
URI: http://oro.open.ac.uk/id/eprint/22531
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