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...

• Type: Conference
• Year: 2002
• Where: KDD
• Authors: Edwin P. D. Pednault, Naoki Abe, Bianca Zadrozny
• Keywords: Cost-sensitive Decisions, Data Mining, Decision Making, KDD 2002, Total Accrued Benefits