Achieving data-driven actionability by combining learning and planning
- 17 Downloads
A main focus of machine learning research has been improving the generalization accuracy and efficiency of prediction models. However, what emerges as missing in many applications is actionability, i.e., the ability to turn prediction results into actions. Existing effort in deriving such actionable knowledge is few and limited to simple action models while in many real applications those models are often more complex and harder to extract an optimal solution.
In this paper, we propose a novel approach that achieves actionability by combining learning with planning, two core areas of AI. In particular, we propose a framework to extract actionable knowledge from random forest, one of the most widely used and best off-the-shelf classifiers. We formulate the actionability problem to a sub-optimal action planning (SOAP) problem, which is to find a plan to alter certain features of a given input so that the random forest would yield a desirable output, while minimizing the total costs of actions. Technically, the SOAP problem is formulated in the SAS+ planning formalism, and solved using a Max-SAT based approach. Our experimental results demonstrate the effectiveness and efficiency of the proposed approach on a personal credit dataset and other benchmarks. Our work represents a new application of automated planning on an emerging and challenging machine learning paradigm.
Keywordsactionable knowledge extraction machine learning planning random forest
Unable to display preview. Download preview PDF.
This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 61502412, 61379066, and 61402395), Natural Science Foundation of the Jiangsu Province (BK20150459, BK20151314, and BK20140492), Natural Science Foundation of the Jiangsu Higher Education Institutions (15KJB520036), United States NSF grants (IIS-0534699, IIS-0713109, CNS-1017701), Microsoft Research New Faculty Fellowship, and the Research Innovation Program for Graduate Student in Jiangsu Province (KYLX16_1390).
- 3.Johnson R A, Gong R, Greatorex-Voith S, Anand A, Fritzler A. A data-driven framework for identifying high school students at risk of not graduating on time. Bloomberg Data for Good Exchange, 2015Google Scholar
- 4.Liu B, Hsu W. Post-analysis of learned rules. In: Proceedings of the AAAI Conference on Artificial Intelligence. 1996, 828–834Google Scholar
- 5.Liu B, HsuW, Ma YM. Pruning and summarizing the discovered associations. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1999, 125–134Google Scholar
- 14.Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R. Intriguing properties of neural networks. In: Proceedings of the International Conference on Learning Representations. 2014Google Scholar
- 18.Friedman J, Hastie T, Tibshirani R. The Elements of Statistical Learning, Vol 1. New York: Springer-Verlag, 2001Google Scholar
- 21.Mohan A, Chen Z, Weinberger K. Web-search ranking with initialized gradient boosted regression trees. Journal of Machine Learning Research, 2011, 14: 77–89Google Scholar
- 30.Kautz H A, Selman B. Planning as satisfiability. In: Proceedings of European Conference on Artificial Intelligence. 1992, 359–363Google Scholar
- 32.Lu Q, Huang R Y, Chen Y X, Xu Y, Zhang W X, Chen G L. A SATbased approach to cost-sensitive temporally expressive planning. ACM Transactions on Intelligent Systems and Technology, 2014, 5(1): 18Google Scholar
- 33.Huang R Y, Chen Y X, Zhang W X. A novel transition based encoding scheme for planning as satisfiability. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2010, 89–94Google Scholar
- 35.Balyo T, Chrpa L, Kilani A. On different strategies for eliminating redundant actions from plans. In: Proceedings of the 7th Annual Symposium on Combinatorial Search. 2014, 10–18Google Scholar