Flexible Frameworks for Actionable Knowledge Discovery

By Longbing Cao, Yanchang Zhao, Huaifeng Zhang, Dan Luo, Chengqi Zhang, E. Park. 2010

100 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

How researchers see this paper

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

In Artificial Intelligence

1. Amir-Hossein Karimi
2. Isabel Valera
3. Bernhard Schölkopf
4. Gilles Barthe
5. Fabrizio Silvestri
6. Andrew Haines
7. Mounia Lalmas
8. Gabriele Tolomei

In Computational Theory and Mathematics

1. Isabel Valera
2. Amir-Hossein Karimi
3. Gilles Barthe
4. Bernhard Schölkopf
5. Dominik Janzing
6. Kun Zhang
7. Jan Peters
8. Dino Sejdinovic

In Information Systems

1. Fabrizio Silvestri
2. Isabel Valera
3. Gilles Barthe
4. Amir-Hossein Karimi
5. Bernhard Schölkopf

Citations in the corpus

• Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking (2017, background): “present domain-driven data mining; a paradigm shi‰ from a research-centered discipline to a practical tool for actionable knowledge [4, 5].”
• 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].”