International Conference on Information Fusion ( FUSION ) Automatically Balancing Accuracy and Comprehensibility in Predictive Modeling

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Automatic-Rule Extraction [217, 251, 252, 253, 254, 255, 256, 319, 320, 321]”
Explaining Explanations: An Overview of Interpretability of Machine Learning (2018, methodology): “Another way to extract rules using sampling methods [32], [33].”
Extended Taxonomy of Rule Extraction Techniques and Assessment of KDRuleEx (2012, methodology): “Opaque models like ANN and SVM shows better accuracy performance in comparison to white box or transparent model like decision tree [1][2][3].”
G-REX: A Versatile Framework for Evolutionary Data Mining (2008, background): “Previous studies [7, 8] have shown that this size punishment allows G-REX to evolve models that are considerably smaller than the corresponding decision trees created using standard algorithms like C4.”
Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning (2018, methodology): “Another way to extract rules using sampling methods [32], [33].”
A Framework for Considering Comprehensibility in Modeling (2016, unknown): “[18] suggests constructing an ensemble of models at various levels of tradeoff, so that appropriate ones can be used.”