Partitioning for Profit: An Empirical Study of Methods for Handling Unequal Costs of Error in Predictive Data Mining

Alan S. Abrahams () and Adrian Becker ()
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Alan S. Abrahams: The Wharton School, University of Pennsylvania
Adrian Becker: University of Pennsylvania

Group Decision and Negotiation, 2007, vol. 16, issue 2, No 5, 209 pages

Abstract: Abstract This paper expands prior work on the Sequential Binary Programming (SBP) algorithm as a framework for cost-sensitive classification. The field of cost-sensitive learning has provided a number of methods to adapt predictive data mining from engineering and hard science applications to those in commerce. This discussion will test theoretical limitations of classical cost-sensitive algorithms empirically and outline the appropriate conditions under which various methods (specifically SBP) should be implemented in favor of others.

Keywords: data mining; decision trees; optimization; target marketing (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1007/s10726-006-9062-6

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