This paper describes a contingency-based approach to ensemble classification. Motivated by a business marketing problem, we explore the use of decision tree models, along with diversity measures and other elements of the task domain, to identify highly-performing ensemble classification models. Working from generated data sets, we found that 1) decision tree models can significantly improve the identification of highly-performing ensembles, and 2) the input parameters for a decision tree are dependent on the characteristics and demands of the decision problem, as well as the objectives of the decision maker.