document: 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 set . The experimental results show that the proposed method for optimizing total accrued benefits out performs the usual targeted-marketing methodology of optimizing each promotion in isolation . We also analyze the behavior of the targeting rules that were obtained and discuss their appropriateness to the application domain .
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