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Data mining with agent gaming

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Abstract

A new type of data mining considers the case where the instances over which induction takes place are intelligent agents who might act strategically to thwart the learner. Instances comprised of humans, companies, or governments all have this capability. One paper calls this adversarial learning and proposes an iterated learning process—much like reinforcement learning—to determine a classifier. The current authors proposed a different approach that uses rational expectation ideas to alter the learner’s problem to directly anticipate possible strategic gaming by agents during the induction process. This paper explores differences between solutions produced by these two approaches on a credit dataset and draws some general insights.

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References

  1. Boylu F, Aytug H, Koehler GJ (2010) Induction over strategic agents. Information Systems Research (forthcoming)

  2. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based methods. Cambridge University Press, Cambridge, UK

    Google Scholar 

  3. Dalvi N, Domingos P, Mausam S, Sanghai S, Verma D (2004) Adversarial classification. In: Tenth ACM SIGKDD international conference on knowledge discovery and data mining (KDD), Seattle, pp 99–108

  4. Mannino M, Koushik M (2000) The cost minimizing inverse classification problem: a genetic algorithm approach. Decis Support Syst 29(3):283–300. doi:10.1016/S0167-9236(00)00077-4

    Article  Google Scholar 

  5. Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, Cambridge, MA

    Google Scholar 

  6. Tharakummel K, Bhattacharyya S (2004) Reinforcement learning in leader-follower multiagent systems. In: Proceedings of the 14th annual workshop on information technologies and systems

  7. Tharakummel K, Bhattacharyya S (20007) Leader-Follower semi-Markov decision problems: theoretical framework and approximate solution. In: Symposium on approximate dynamic programming and reinforcement learning. IEEE symposium series on computational intelligence

  8. Von Stackelberg H (1952) The theory of market economy. Oxford University Press, London, UK

    Google Scholar 

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Authors and Affiliations

  1. Operations and Information Management, School of Business, University of Connecticut, Storrs, CT, USA

    Fidan Boylu

  2. Information Systems and Operations Management Department, 351 Stuzin, The Warrington College of Business Administration, University of Florida, Gainesville, FL, 32611, USA

    Haldun Aytug & Gary J. Koehler

Authors
  1. Fidan Boylu
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  2. Haldun Aytug
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  3. Gary J. Koehler
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Corresponding author

Correspondence to Fidan Boylu.

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Boylu, F., Aytug, H. & Koehler, G.J. Data mining with agent gaming. Inf Technol Manag 11, 1–6 (2010). https://doi.org/10.1007/s10799-010-0064-3

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  • DOI: https://doi.org/10.1007/s10799-010-0064-3

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