Extracting Actionable Knowledge from Decision Trees

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How researchers see this paper

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

Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking (2017, background): “Many works discuss post-processing techniques specically tailored to decision trees [8, 16, 24, 25].”
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, methodology): “Actionable knowledge extraction is employed in data mining to suggest “behaviors which render a state of an instance into a preferred state” [57] according to a classifier [36–38, 90, 186].”
Sensor-Based Abnormal Human-Activity Detection (2008, methodology): “In addition, we will also investigate how to best integrate intelligent planning for courses of actions when an abnormal situation is discovered using statistical methods such as the actionable data mining algorithms in [29].”
Flexible Frameworks for Actionable Knowledge Discovery (2010, methodology): “Additionally, rather than addressing the whole KDD process as it did in some of related works, this paper only focuses on method/algorithm frameworks toward AKD.”
Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing (2013, methodology): “Knowledge Discovery in Databases (KDD) [3,6,13,14] is an active area of research that resolves the complexity mentioned above.”