Sequential Supervised Learning
I need help on how to model this particular problem in RapidMiner. Here's sample data I'm trying to model in RapidMiner:
id sequence rank
1 1020110201 40
2 0010120100 34
3 2110100110 18
4 0120010110 -13
5 0101010020 -98
6 0101210010 -21
As you can see the sequence consists of 10 digits with '0', '1', and '2' items but a sequence is associated with a single rank value (either positive or negative) e.g. 1020110201->40. As for the example above, the intention is to classify all past sequences with their corresponds ranks. So for example, given a new sequence 0101211010 the classifier should be able to predict the rank.
What is the best way to model in this rapidminer? Right now (following the neural trend tutorial) I assigned rank as the label and i used 10 different attributes to capture a single sequence but i'm not sure if this is the correct way as in my case, the sequence string exhibits significant sequential correlation.
Your help is very much appreciated.