MultipleLabelIterator: how to specify positive/negative attribute values?
New Altair Community Member
Updated by Jocelyn
I'm using the MultipleLabelIterator, following the sample 07_Meta/05_MultipleLabelLearning.xml. However, I'm pulling my data from a database using DatabaseExampleSource. Then I apply ChangeAttributeRole operators to each attribute to make it of type 'label1', 'label2', and so on. The result looks like the sample dataset with 'positive' or 'negative' nominal features depending on whether each row exemplifies the given feature.
When run, RapidMiner fails on the AverageBuilder operator: "Cannot build average for different positive classes (positive/negative)."
Looking at the datasets I see that in the sample data, the Range for each label# feature is always "positive(##), negative(##)". In my dataset, I see that some features are listed as "negative(##), positive(##)".
It seems that RapidMiner is not relating the values 'positive' and 'negative' but instead is using their positions, which are loading inconsistently.
Is there a way to tell RapidMiner which nominal value is the positive classname? Or another way to work around this error?
Thanks,
Gary
When run, RapidMiner fails on the AverageBuilder operator: "Cannot build average for different positive classes (positive/negative)."
Looking at the datasets I see that in the sample data, the Range for each label# feature is always "positive(##), negative(##)". In my dataset, I see that some features are listed as "negative(##), positive(##)".
It seems that RapidMiner is not relating the values 'positive' and 'negative' but instead is using their positions, which are loading inconsistently.
Is there a way to tell RapidMiner which nominal value is the positive classname? Or another way to work around this error?
Thanks,
Gary