Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data

• URL: http://arxiv.org/abs/2208.02694v1
• Date: Thu, 4 Aug 2022 14:48:37 GMT
• Title: Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data
• Authors: Tom\'a\v{s} Pevn\'y and Viliam Lis\'y and Branislav Bo\v{s}ansk\'y and Petr Somol and Michal P\v{e}chou\v{c}ek
• Abstract summary: A number of data sources have the natural form of structured data interchange formats (e.g. Multiple security logs in/XML format) Existing methods, such as in Hierarchical Instance Learning (HMIL) allow learning from such data in their raw form. By treating these models as sub-set selections problems, we demonstrate how interpretable explanations, with favourable properties, can be generated using computationally efficient algorithms. We compare to an explanation technique adopted from graph neural networks showing an order of magnitude speed-up and higher-quality explanations.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Learning from raw data input, thus limiting the need for feature engineering, is a component of many successful applications of machine learning methods in various domains. While many problems naturally translate into a vector representation directly usable in standard classifiers, a number of data sources have the natural form of structured data interchange formats (e.g., security logs in JSON/XML format). Existing methods, such as in Hierarchical Multiple Instance Learning (HMIL), allow learning from such data in their raw form. However, the explanation of the classifiers trained on raw structured data remains largely unexplored. By treating these models as sub-set selections problems, we demonstrate how interpretable explanations, with favourable properties, can be generated using computationally efficient algorithms. We compare to an explanation technique adopted from graph neural networks showing an order of magnitude speed-up and higher-quality explanations.

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