Efficient Action Extraction with Many-to-Many Relationship between Actions and Features

By Jianfeng Du, Yong Hu, Charles X. F. Ling, Ming Fan, Mei Liu. 2011

9 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

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. Bernhard Schölkopf
2. Amir-Hossein Karimi
3. Isabel Valera
4. Gilles Barthe
5. Yixin Chen
6. Zhicheng Cui
7. Dominik Janzing
8. Arthur Gretton

In Computer Vision and Pattern Recognition

1. Yixin Chen
2. Isabel Valera
3. Gilles Barthe
4. Amir-Hossein Karimi
5. Bernhard Schölkopf
6. Yujie He
7. Zhicheng Cui
8. Dacheng Tao

In Signal Processing

1. Isabel Valera
2. Gilles Barthe
3. Amir-Hossein Karimi
4. Bernhard Schölkopf
5. Zhicheng Cui
6. Yixin Chen
7. Arthur Gretton
8. Dominik Janzing

Citations in the corpus

• A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, background): “ssify it as a member of a different preferred class?” [118]. Actionableknowledgeextraction 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]. Finally, adversarial perturbations are small imperc”
• Optimal Action Extraction for Random Forests and Boosted Trees (2015, background): “Techniques have also been proposed to postprocess decision trees to extract actionable knowledge [31, 19, 30, 12].”