What criteria can be used to decide whether to develop a decision tree or one or more rules using oneR or jRip(contained in RWeka). The similarities between these two algorithms (decision tree and rule) is very high making it a little confusing.
JRip implements a propositional rule learner, “Repeated Incremental Pruning to Produce Error Reduction” (RIPPER), as proposed by Cohen (1995) and OneR builds a simple 1-R classifier, proposed by Holte (1993).
Its hard to say which algorithm works better. Best approach is to compare different classification algorithms performance in terms of precision, recall, accuracy, f1 score, AUC, specificity and sensitivity on your train/test data set and pick the one that gives best results or use ensemble of top performing algorithms to build your final model.
There is a good white paper doing similar exercise of comparing different classification algorithms including OneR and JRIP here.
Hope this helps.