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- W4239976036 abstract "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." @default.
- W4239976036 created "2022-05-12" @default.
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- W4239976036 date "2002-01-01" @default.
- W4239976036 modified "2023-09-27" @default.
- W4239976036 title "Sequential cost-sensitive decision making with reinforcement learning" @default.
- W4239976036 doi "https://doi.org/10.1145/775085.775086" @default.
- W4239976036 hasPublicationYear "2002" @default.
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