FIT: Using Feature Importance to Teach Classification Tasks to Unknown Learners

Carla Guerra, Francisco S. Melo, Manuel Lopes

Abstract: This work introduces an interactive machine teaching approach that teaches classification tasks. But instead of assuming perfect knowledge about the learner as most machine teaching approaches do, our adaptive approach—Feature Importance Teaching (FIT)—chooses the samples to show based on a model of the learner updated online using feedback about the weights attributed to the features. We run simulations where there is a mismatch on the prior knowledge and learning model of the student and the ones assumed by the teacher. The results have shown that our teaching approach can mitigate this mismatch and lead to significantly faster learning curves than the ones obtained in conditions where the teacher randomly selects the samples or does not consider this kind of feedback from the student. We tested using data sets from two different application domains and the conclusions were the same. We also tested FIT when the student provides only the most important feature and it still outperformed the other approaches considered. We finally conducted a study with real human users, which confirmed the results obtained in the simulations.