The Mimetic Technique when the Original Data is Not Available: Model Learning and Revision

Abstract

The mimetic technique (or Multiple Combined Models, CMM) basically consists of using a generally accurate but incomprehensible model as an oracle to generate and label a random data set. This dataset is used, along with the original training data, to train a second comprehensible model, known as the mimetic model. This technique has been used to provide understandability to black box models without considerably sacrificing their accuracy. In this work we study the mimetic application in a scenario in which the original training data is not available. In this context we first determine the optimal size of the random data set, according to the minimum message length principle (MML). This result can be used in knowledge acquisition for expert systems. Secondly we apply the mimetic technique to model revision and show that in some change situations the mimetic model can be used as a transition model between the original model and the new model.