In Rapidminer, are the decision tree's weights a measure of the "importance" of attributes in the splitting procedure ? If yes, why is useful to know these weights ? Are there better methods to know the most discriminant features in a data set ?

I'm new to data mining, so this might sound like a very simple task to some. I work in reliability engineering in aviation and have a set of data that is generated on a daily basis regarding system failures and failure rectification. This data is categorized using numerical tags of maintenance manual tasks (reference data) by chapter, section, and paragraph. However, since the data is entered manually by people, sometimes, the wrong chapter/section tags are entered and would require being …