Knowledge actionability: satisfying technical and business interestingness

By Longbing Cao, Dan Luo, Chengqi Zhang. 2007

62 citations recorded by the source corpus.

Subfields: Information Systems, Management Information Systems, Strategy and Management

How researchers see this paper

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

In Information Systems

1. Bernhard Schölkopf
2. Isabel Valera
3. Gilles Barthe
4. Amir-Hossein Karimi
5. Yixin Chen
6. Fabrizio Silvestri
7. Dan Luo
8. Yujie He

In Management Information Systems

1. Bernhard Schölkopf
2. Longbing Cao

In Strategy and Management

1. Isabel Valera
2. Bernhard Schölkopf
3. Longbing Cao

Citations in the corpus

• Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking (2017, background): “Early works have focused on the development of interestingness metrics as proxy measures of knowledge actionability [3, 10].”
• 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].”
• Optimal Action Extraction for Random Forests and Boosted Trees (2015, background): “Cao et al. highlighted both technical interestingness and domain-specific expectations and proposed a two-way framework to measure knowledge actionablility [5].”
• Flexible Frameworks for Actionable Knowledge Discovery (2010, methodology): “One essential way is to develop effective approaches for discovering patterns that not only are of technical significance [35], but also satisfy business expectations [14], and further indicate the possible actions that can be explicitly taken by business people [1], [6].”