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Partitioning for Profit: An Empirical Study of Methods for Handling Unequal Costs of Error in Predictive Data Mining |
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| Authors: | Alan S. Abrahams Adrian Becker |
| Affiliation: | (1) Department of Operations and Information Management, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA;(2) Department of Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA |
| 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 |
| 本文献已被 SpringerLink 等数据库收录！ | |