Metafeatures-based Rule-Extraction for Classifiers on Behavioral and Textual Data

• URL: http://arxiv.org/abs/2003.04792v3
• Date: Thu, 4 Mar 2021 12:15:48 GMT
• Title: Metafeatures-based Rule-Extraction for Classifiers on Behavioral and Textual Data
• Authors: Yanou Ramon, David Martens, Theodoros Evgeniou, Stiene Praet
• Abstract summary: Rule-extraction techniques have been proposed to combine the desired predictive accuracy of complex "black-box" models with global explainability. We develop and test a rule-extraction methodology based on higher-level, less-sparse metafeatures. A key finding of our analysis is that metafeatures-based explanations are better at mimicking the behavior of the black-box prediction model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning models on behavioral and textual data can result in highly accurate prediction models, but are often very difficult to interpret. Rule-extraction techniques have been proposed to combine the desired predictive accuracy of complex "black-box" models with global explainability. However, rule-extraction in the context of high-dimensional, sparse data, where many features are relevant to the predictions, can be challenging, as replacing the black-box model by many rules leaves the user again with an incomprehensible explanation. To address this problem, we develop and test a rule-extraction methodology based on higher-level, less-sparse metafeatures. A key finding of our analysis is that metafeatures-based explanations are better at mimicking the behavior of the black-box prediction model, as measured by the fidelity of explanations.

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