Teaching Unknown Learners to Classify via Feature Importance

In this work we introduce an interactive machine teaching approach that teaches a classification task to the learner. Our adaptive approach Feature Importance Teaching (FIT) does not assume perfect knowledge about the learner, as most machine teaching approaches do. It chooses, online, which sample to show next, as it updates the learner’s model based on feedback from the student on the weights attributed to the features. We present simulated results where the student has a different prior knowledge from the one assumed by the teacher. The results have shown that our teaching approach can mitigate this mismatch and lead to a significantly faster learning curve than the ones obtained in conditions where the teacher randomly selects the samples or does not consider this feedback from the student.