  Binary classification rules based on covariates typically depend on simple
loss functions such as zero-one misclassification. Some cases may require more
complex loss functions. For example, individual-level monitoring of
HIV-infected individuals on antiretroviral therapy (ART) requires periodic
assessment of treatment failure, defined as having a viral load (VL) value
above a certain threshold. In some resource limited settings, VL tests may be
limited by cost or technology, and diagnoses are based on other clinical
markers. Depending on scenario, higher premium may be placed on avoiding
false-positives which brings greater cost and reduced treatment options. Here,
the optimal rule is determined by minimizing a weighted misclassification
loss/risk.
  We propose a method for finding and cross-validating optimal binary
classification rules under weighted misclassification loss. We focus on rules
comprising a prediction score and an associated threshold, where the score is
derived using an ensemble learner. Simulations and examples show that our
method, which derives the score and threshold jointly, more accurately
estimates overall risk and has better operating characteristics compared with
methods that derive the score first and the cutoff conditionally on the score
especially for finite samples.
