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Activity Number: 243
Type: Contributed
Date/Time: Tuesday, August 8, 2006 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #306470
Title: An Investigation of Missing Data Methods for Decision Trees
Author(s): Yufeng Ding*+ and Jeffrey Simonoff
Companies: New York University and New York University
Address: 44 W. 4th Street, New York, NY, 10012,
Keywords: missing data ; decision tree ; probabilistic split ; surrogate split ; CART ; c4.5
Abstract:

There are many different missing data methods used by decision tree algorithms, but not many studies have been done on their appropriateness and performance. This paper provides both analytic and Monte Carlo evidence regarding the effectiveness of six popular missing data methods. We show that in the context of decision trees, the relationship between the missingness and the dependent variable, rather than the standard missingness classification approach of Rubin (missing completely at random, missing at random, and not missing at random), is the most helpful criterion to distinguish different missing data methods. We are also able to make recommendations as to the best method to use in various situations.

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