DM: missing attribute values in classification trees

[Tjen-Sien Lim]

Hi, I'd like to get some advice from those of you who have analyzed
datasets with missing attribute values in supervised learning.

What's the "typical" proportion of cases with missing values? How big
the proportion has to be before it presents problems?

We're conducting a project comparing classification trees classifiers
on datasets with missing values. We'd like to simulate
missing-at-random on datasets that contain no missing values to
increase the number of datasets. Should we simulate 5%, 10%, 20%, or
30% missing at random? Any preferred way to induce those missing
values?

Thanks in advance for any advice/pointers/suggestions.

-- 
Tjen-Sien Lim                (608) 262-8181                        
Ph.D. candidate              limt@stat.wisc.edu                    
Dept. of Statistics          http://www.stat.wisc.edu/~limt
Univ. of Wisconsin-Madison
1210 West Dayton Street       
Madison, WI 53706