I was doing a machine learning task in Weka and the dataset has 486 attributes. So, I wanted to do attribute selection using chi-square and it provides me ranked attributes like below: 
Now, I also have a testing dataset and I have to make it compatible. But how can I reorder the test attributes in the same manner that can be compatible with the train set?
Changing the order of attributes (e.g., when using the Ranker in conjunction with an attribute evaluator) will probably not have much influence on the performance of your classifier model (since all the attributes will stay in the dataset). Removing attributes, on the other hand, will more likely have an impact (for that, use subset evaluators).
If you want the ordering to get applied to the test set as well, then simply define your attribute selection search and evaluation schemes in the AttributeSelectedClassifier meta-classifier, instead of using the Attribute selection panel (that panel is more for exploration).
