Abstract
Direct marketing is one of the most common and crucial business intelligence tasks. In direct marketing, the goal of an agent is to mine the right customers to market certain products, with the goal of making fewest mistakes. This data-mining problem, though similar to active learning in terms of allowing the agent to select customers actively, is, in fact, opposite to active learning. As far as we know, no previous data mining algorithms can solve this problem well. In this paper, we propose a simple yet effective algorithm called Most-Certain Learning (MCL) to handle this type of problems. The experiments show that our data-mining algorithms can solve various direct marketing problems effectively.
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Ni, E.A., Ling, C.X. (2011). Direct Marketing with Fewer Mistakes. In: Tang, J., King, I., Chen, L., Wang, J. (eds) Advanced Data Mining and Applications. ADMA 2011. Lecture Notes in Computer Science(), vol 7120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25853-4_20
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DOI: https://doi.org/10.1007/978-3-642-25853-4_20
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