Re: How to classify unseen multi instance data?

Hi Peter,

Am 25.01.24 um 03:56 schrieb Peter Reutemann:
> 
> Even for multi-instance data you only use a single Instance object,
> representing a single bag, for making predictions.

My confusion stemmed from the somewhat unfortunate terminology, whereby 
I thought an Instance object is single-instance and Instances is 
multi-instance - which I now believe to be wrong. An Instance can either 
be single- or multi-instance and if it is multi-instance we also call it 
a bag. Instances is then either:

- a bunch of bags or
- a bunch of single-instance Instance objects

The next point to note is that training a model is done off an Instances 
object, whereas classification of unseen data is using an Instance. 
Therefore the answer to my original question appears to be thus:

When training a model, first get the data from somewhere (ARFF file, 
database, ...) and then convert it to an Instances object. When 
classifying unseen data, use the exact same code to create an Instances 
object (optionally skipping the class values, which are not needed 
here). Then it's just a matter of passing Instances.firstInstance() to 
the classifier. Hopefully I explained it correctly now :)

FWIW, from a software engineering standpoint single- and multi-instance 
data should be distinguishable by their type (Java class). 
Multi-instance data should probably implement an additional interface.

Kind regards, Ulrich

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