1. A method for building a decision tree from an input data set, the input data set comprising records and associated attributes, the attributes including a class label attribute for indicating whether a given record is a member of a target class or a non-target class, the input data set being biased in favor of the records of the non-target class, the decision tree comprising a plurality of nodes that include a root node and leaf nodes, said method comprising the steps of: constructing the decision tree from the input data set, including the step of partitioning each of the plurality of nodes of the decision tree, beginning with the root node, based upon multivariate subspace splitting criteria; computing distance functions for each of the leaf nodes; identifying, with respect to the distance functions, a nearest neighbor set of nodes for each of the leaf nodes based upon a respective closeness of the nearest neighbor set of nodes to a target record of the target class; and classifying and scoring the records, based upon the decision tree and the nearest neighbor set of nodes.