Hi there. I was wondering if somebody knows how to perform a bagging procedure on a classification tree without running the classifier with weights.
Let me first explain why I need this and then give some details of what I have found out so far. I am thinking about implementing the bagging procedure in Matlab. Matlab has a simple classification tree function (in their Statistics toolbox) but it does not accept weights. A modification of the Matlab procedure to accommodate weights would be very complicated. The rpart function in R accepts weights. This seems to allow for a rather simple implementation of bagging. In fact Everitt and Hothorn in chapter 8 of "A Handbook of Statistical Analyses Using R" describe such a procedure. The procedure consists in generating several samples with replacement from the original data set. This data set has N rows. The implementation described in the book first fits a non-pruned tree to the original data set. Then it generates several (say, 25) multinomial samples of size N with probabilities 1/N. Then, each sample is used in turn as the weight vector to update the original tree fit. Finally, all the updated trees are combined to produce "consensus" class predictions. Now, a typical realization of a multinomial sample consists of small integers and several 0's. I thought that the way that weighting worked was this: the observations with weights equal to 0 are omitted and the observations with weights > 1 are essentially replicated according to the weight. So I thought that instead of running the rpart procedure with weights, say, starting with (1, 0, 2, 0, 1, ... etc.) I could simply generate a sample data set by retaining row 1, omitting row 2, replicating row 3 twice, omitting row 4, retaining row 5, etc. However, this does not seem to work as I expected. Instead of getting identical trees (from running weighted rpart on the original data set and running rpart on the sample data set described above with no weighting) I get trees that are completely different (different threshold values and different order of variables entering the splits). Moreover, the predictions from these trees can be different so the misclassification rates usually differ. This finally brings me to my question - is there a way to mimic the workings of the weighting in rpart by, for example, modification of the data set or, perhaps, some other means. Thanks in advance for your time, Andy __________________________________ Andy Jaworski 518-1-01 Process Laboratory 3M Corporate Research Laboratory ----- E-mail: [EMAIL PROTECTED] Tel: (651) 733-6092 Fax: (651) 736-3122 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
