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KDD
2002
ACM

Sequential cost-sensitive decision making with reinforcement learning

9 years 3 months ago
Sequential cost-sensitive decision making with reinforcement learning
Recently, there has been increasing interest in the issues of cost-sensitive learning and decision making in a variety of applications of data mining. A number of approaches have been developed that are effective at optimizing cost-sensitive decisions when each decision is considered in isolation. However, the issue of sequential decision making, with the goal of maximizing total benefits accrued over a period of time instead of immediate benefits, has rarely been addressed. In the present paper, we propose a novel approach to sequential decision making based on the reinforcement learning framework. Our approach attempts to learn decision rules that optimize a sequence of cost-sensitive decisions so as to maximize the total benefits accrued over time. We use the domain of targeted marketing as a testbed for empirical evaluation of the proposed method. We conducted experiments using approximately two years of monthly promotion data derived from the well-known KDD Cup 1998 donation data...
Edwin P. D. Pednault, Naoki Abe, Bianca Zadrozny
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2002
Where KDD
Authors Edwin P. D. Pednault, Naoki Abe, Bianca Zadrozny
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