Re: Weka Experimenter - Classification for multiple Datasets

Yes, in your first experiment, WEKA will treat that two datasets as completely separate entities.

If I understand you correctly, the result from your second experiment implies that there is an attribute in your data (assuming you are using OneR) that can perfectly separate the "true" and "false" cases in your (one) dataset. To verify this and check which attribute is responsible, try repeating the experiment in the Classify panel of the WEKA Explorer.

It sounds to me like you might want to look at multi-instance learning (also called "multiple instance" learning), which is designed to classify bags (i.e., "datasets"). WEKA has some learning algorithms that support it and relational (aka "relation-valued") attributes are available whose values can be entire datasets:

https://waikato.github.io/weka-wiki/formats_and_processing/arff_stable/

https://waikato.github.io/weka-wiki/multi_instance_classification/

Cheers,
Eibe
Multi instance classification - Weka Wiki<https://waikato.github.io/weka-wiki/multi_instance_classification/>
Documention on Weka that can also be found in the manual and the example archive that comes with each Weka download (zip or installer).
waikato.github.io

Arff stable - Weka Wiki<https://waikato.github.io/weka-wiki/formats_and_processing/arff_stable/>
Documention on Weka that can also be found in the manual and the example archive that comes with each Weka download (zip or installer).
waikato.github.io


________________________________
From: Ulrich Mayring <[email protected]>
Sent: Saturday, November 11, 2023 2:16 AM
To: [email protected] <[email protected]>
Subject: [Wekalist] Weka Experimenter - Classification for multiple Datasets

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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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