Re: DM: missing attribute values in classification trees

[Lynd D Bacon]

Tjen-Sien, it's often the case with real-world data that what's missing isn't missing at random.  So, it might be interesting and useful to consider also the effects of varying degrees of dependence between the probabilities of different variables being missing.

-lynd bacon

At 12:21 PM 10/29/98 -0600, you wrote:

>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

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