Weka Experimenter - Classification for multiple Datasets

Hi all,

this is a very high-level question about how the classification in Weka 
works with multiple input files:

Are they treated as seperate (non-related) datasets or are they combined 
into one dataset? I couldn't tell from experimenting with it.

Background: I have ARFF files, which contain data like this:

@attribute sensordata1 numeric
@attribute sensordata2 numeric
...
@attribute class {true,false}

So I have many of those files with all different sensordata values, but 
within each file the class attribute is always either true or false for 
all rows. So now I want to train a model in order to classify future 
files (which do not contain the class attribute) as true or false.

So here's what I did:

I loaded two files into the experimenter (one with the class attribute 
as true and one with false) and the result was a 100% success rate in 
classification with either OneR or J48 (using ten-fold cross 
validation). But both datasets were listed seperately in the results 
window, which makes me believe that were evaluated seperately and the 
100% success rate is because the class value is the same for all rows.

But then I combined both files into one file and loaded that one file 
into the experimenter and the result was the same. I would have expected 
a success rate below 100%, because now half of the rows were now "true" 
and the other half "false".

So my question is: did I actually run the same experiment twice or does 
Weka behave differently with multiple datasets compared to one dataset 
(even if the actual data is the same in both cases)?

Many thanks in advance for any pointers,

Ulrich

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