2015 OptimalActionExtractionforRando

• (Cui et al., 2015) ⇒ Zhicheng Cui, Wenlin Chen, Yujie He, and Yixin Chen. (2015). “Optimal Action Extraction for Random Forests and Boosted Trees.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783281

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Optimal+Action+Extraction+for+Random+Forests+and+Boosted+Trees
• http://dl.acm.org/citation.cfm?id=2783258.2783281&preflayout=flat#citedby

Quotes

Author Keywords

• Actionable knowledge; additive tree model; data mining; decision tree; integer linear programming; random forest

Abstract

Additive tree models (ATMs) are widely used for data mining and machine learning. Important examples of ATMs include random forest, adaboost (with decision trees as weak learners), and gradient boosted trees, and they are often referred to as the best off-the-shelf classifiers. Though capable of attaining high accuracy, ATMs are not well interpretable in the sense that they do not provide actionable knowledge for a given instance. This greatly limits the potential of ATMs on many applications such as medical prediction and business intelligence, where practitioners need suggestions on actions that can lead to desirable outcomes with minimum costs.

To address this problem, we present a novel framework to post-process any ATM classifier to extract an optimal actionable plan that can change a given input to a desired class with a minimum cost. In particular, we prove the NP-hardness of the optimal action extraction problem for ATMs and formulate this problem in an integer linear programming formulation which can be efficiently solved by existing packages. We also empirically demonstrate the effectiveness of the proposed framework by conducting comprehensive experiments on challenging real-world datasets.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 OptimalActionExtractionforRando	Yixin Chen Wenlin Chen Zhicheng Cui Yujie He			Optimal Action Extraction for Random Forests and Boosted Trees				10.1145/2783258.2783281		2015