N Rules learner. This method permits, like decision trees, to extract complex rules; however the accuracy of the predictions is high only if the rules have a sufficiently large support. Moreover, it can be computationally demanding in case of large datasets. In this work we applied the PART method to generate a decision list. Such method is based on an iterative strategy. In each step, PART builds a partial decision tree and converts the best leaf into a rule [48] . The minimum number of instances for each leaf was fixed at 1% of the examples in order to limit the number of generated rules.