  This study uses stacked generalization, which is a two-step process of
combining machine learning methods, called meta or super learners, for
improving the performance of algorithms in step one (by minimizing the error
rate of each individual algorithm to reduce its bias in the learning set) and
then in step two inputting the results into the meta learner with its stacked
blended output (demonstrating improved performance with the weakest algorithms
learning better). The method is essentially an enhanced cross-validation
strategy. Although the process uses great computational resources, the
resulting performance metrics on resampled fraud data show that increased
system cost can be justified. A fundamental key to fraud data is that it is
inherently not systematic and, as of yet, the optimal resampling methodology
has not been identified. Building a test harness that accounts for all
permutations of algorithm sample set pairs demonstrates that the complex,
intrinsic data structures are all thoroughly tested. Using a comparative
analysis on fraud data that applies stacked generalizations provides useful
insight needed to find the optimal mathematical formula to be used for
imbalanced fraud data sets.
