Handling Generalized Cost Functions in the Partitioning Optimization Problem Through Sequential Binary Programming Alan Abrahams, Adrian Becker, Daniel Fleder, and Ian MacMillan   View Paper (PDF)   Return to Session 21: Optimization Techniques Abstract This paper proposes a framework for cost-sensitive classification under a generalized cost function. By combining decision trees with sequential binary programming, we can handle unequal misclassification costs, constrained classification, and complex objective functions that other methods cannot. Our approach has two main contributions. First, it provides a new method for cost-sensitive classification that outperforms a traditional, accuracy-based method and some current cost-sensitive approaches. Second, and more important, our approach can handle a generalized cost function, instead of the simpler misclassification cost matrix to which other approaches are limited. ©2006 Association for Computing Machinery