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Achieving data-driven actionability by combining learning and planning

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Abstract

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.

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Acknowledgments

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).

Author information

Authors and Affiliations

  1. College of Information Engineering, Yangzhou University, Yangzhou, 225127, China

    Qiang Lv, Zhaorong Li & Ling Chen

  2. Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA

    Yixin Chen & Zhicheng Cui

  3. School of Management, Fudan University, Shanghai, 200433, China

    Xing Zhang

  4. School of Computer and Control Engineering, University of Chinese Academy of Science, Beijing, 100049, China

    Haihua Shen

Authors
  1. Qiang Lv
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  2. Yixin Chen
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  3. Zhaorong Li
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  4. Zhicheng Cui
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  5. Ling Chen
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  6. Xing Zhang
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  7. Haihua Shen
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Corresponding authors

Correspondence to Qiang Lv or Haihua Shen.

Additional information

Qiang Lv is currently an assistant professor in College of Information Engineering, Yangzhou University, China. He received the BE and PhD degrees from the School of Computer Science and Technology, University of Science and Technology of China, China in 2007 and 2012, respectively. His research interests include data mining, automated planning and scheduling. He has published more than ten papers in journals and conference proceedings, including ACM TIST, IEEE TSC, EAAI, FCS, AAAI’13, ICAPS’11, Cloud-Com’11, and IPC’11. He is a member of the ACM and the CCF.

Yixin Chen is a professor of computer science at the Washington University in St. Louis, USA. He received the PhD degree in computer science from the University of Illinois at Urbana-Champaign, USA in 2005. His research interests include nonlinear optimization, constrained search, planning and scheduling, data mining, and data warehousing. His work on planning has won First-Class Prizes in the International Planning Competitions (2004 and 2006), the Best Paper Award in AAAI (2010) and ICTAI (2005), and Best Paper nomination at KDD (2009). He has received an Early Career Principal Investigator Award from the Department of Energy (2006) and a Microsoft Research New Faculty Fellowship (2007). Dr. Chen is a senior member of IEEE. He serves as an associate editor of IEEE Transactions on Knowledge and Data Engineering, and ACM Transactions on Intelligent Systems and Technology.

Zhaorong Li is currently a graduate student in the College of Information Engineering, Yangzhou University (YZU), China. She received the Bachelor’s degree from the College of Guangling at YZU. Her research interests include data mining, machine learning and artificial intelligence. She has published two papers in Journal of Chinese Computer Systems and CCDM. She is a student member of the CCF.

Zhicheng Cui received his BE degree in computer science from University of Science and Technology of China, China in 2014. He is now a PhD candidate in the Department of Computer Science and Engineering, Washington University in St. Louis (WUSTL), USA, supervised by Prof. Yixin Chen. His research interests are data mining and machine learning, in the area of large scale time series analysis.

Ling Chen is currently a professor in the College of Information Engineering, Yangzhou University, China. His research interests include bioinformatics, data mining and computational intelligence. He has co-edited six books/proceedings, and published more than 300 research papers including over 120 journal papers. He has received many awards from government and agencies. He has organized several academic conferences and workshops and has also served as a program committee chair or member for several major international conferences. He is a member of IEEE CS society and ACM, and a senior member of the Chinese Computer Society.

Xing Zhang is an assistant professor of marketing at the School of Management, Fudan University, China. She received the PhD degree in marketing from Washington University in St. Louis, USA in 2013. Her research interests are in empirical modeling consumer behavior and firm competition using econometric methodologies. She has conducted various research projects in the domain of marketing and economics. Her research about consumer information search and firm pricing has been published in Management Science.

Haihua Shen is an associate professor with the School of Computer and Control Engineering, University of Chinese Academy of Sciences, China. She received the PhD degree in computer science and technology from Tsinghua University, China in 2002. Her research interests include computer architecture, artificial intelligence, VLSI design & verification and hardware security. She has published more than 30 technical papers, and holds 20 patents.

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Lv, Q., Chen, Y., Li, Z. et al. Achieving data-driven actionability by combining learning and planning. Front. Comput. Sci. 12, 939–949 (2018). https://doi.org/10.1007/s11704-017-6315-2

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