Postprocessing decision trees to extract actionable knowledge

By Qiang Yang, Jie Yin, Charles X. F. Ling, Tielin Chen. 2003

92 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, 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 Computer Vision and Pattern Recognition

1. Mounia Lalmas
2. Fabrizio Silvestri
3. Andrew Haines
4. Yixin Chen
5. Qiang Yang
6. Charles X. F. Ling
7. Rong Pan
8. Yujie He

In Information Systems

1. Longbing Cao
2. Chengqi Zhang
3. Dan Luo
4. Qiang Yang
5. Yixin Chen
6. Jie Yin
7. Rong Pan
8. Fabrizio Silvestri

In Signal Processing

1. Qiang Yang
2. Fabrizio Silvestri
3. Charles X. F. Ling
4. Jie Yin
5. Longbing Cao
6. Chengqi Zhang
7. Zhicheng Cui
8. Yixin Chen

Citations in the corpus

• Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking (2017, background): “Many works discuss post-processing techniques speci€cally tailored to decision trees [8, 16, 24, 25].”
• Knowledge actionability: satisfying technical and business interestingness (2007, methodology): “…measures such as unexpectedness (Silberschatz and Tuzhilin, 1995; Padmanabhan and Tuzhilin, 1998, 1999), actionability (Silberschatz and Tuzhilin, 1995, 1996; Yang et al., 2003; Cao and Zhang, 2006, 2007) and novelty (Tuzhilin, 2002) were studied to evaluate pattern actionable capability.”
• 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].”
• Extracting Actionable Knowledge from Decision Trees (2007, background): “Actions, such as direct mailing and sales promotion, cost money to the enterprise [42].”