Model Agnostic Interpretability for Multiple Instance Learning

• URL: http://arxiv.org/abs/2201.11701v2
• Date: Fri, 28 Jan 2022 09:58:57 GMT
• Title: Model Agnostic Interpretability for Multiple Instance Learning
• Authors: Joseph Early, Christine Evers and Sarvapali Ramchurn
• Abstract summary: In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. In this work, we establish the key requirements for interpreting MIL models. We then go on to develop several model-agnostic approaches that meet these requirements.
• Score: 7.412445894287708
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. A bag label is often only determined by a handful of key instances within a bag, making it difficult to interpret what information a classifier is using to make decisions. In this work, we establish the key requirements for interpreting MIL models. We then go on to develop several model-agnostic approaches that meet these requirements. Our methods are compared against existing inherently interpretable MIL models on several datasets, and achieve an increase in interpretability accuracy of up to 30%. We also examine the ability of the methods to identify interactions between instances and scale to larger datasets, improving their applicability to real-world problems.

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