I am trying to use machine learning on some peculiar (at least for me) data. Usually, when I do machine learning I am use to have the data in this format:
Feat1 Feat2 Feat3 Feat4 Class I1 | .0 | .0 | .2 | .3 | TRUE | I2 | .5 | .0 | .1 | .3 | TRUE | I3 | .0 | .0 | .1 | .3 | FALSE |
In this case, my data is in this format:
F1 F2 F3 Class I1 a | .0 | .2 | .3 | b | .0 | .2 | .3 | c | .0 | .2 | .3 | TRUE | I2 a | .9 | .1 | .1 | b | .0 | .0 | .0 | c | .0 | .0 | .0 | TRUE | I3 a | .0 | .0 | .1 | b | .0 | .2 | .3 | c | .0 | .2 | .1 | FALSE |
So I have multiple rows concurring to the final classification of an instance, and I can compute the features only at this sub-level.
One way to aggregate the features in a single row would be to, for example, using things such as 'mean', 'min/max', etc. But there is no clear choice for these aggregations in our dataset.
The number of rows is not known a-priori, so I cannot create a single row aggregating the multiple rows to model the instance. For example, I cannot have:
F1-a F2-a F3-a F1-b F2-b F3-b F1-c F2-c F3-c Class I1 a | .0 | .2 | .3 | .0 | .2 | .3 | .0 | .2 | .3 | TRUE |
Moreover, this does not seem to be a multiple-instance learning problem, because I am not trying to classify the single row, but the instances (which are made of features from multiple rows).