  Inverse classification, the process of making meaningful perturbations to a
test point such that it is more likely to have a desired classification, has
previously been addressed using data from a single static point in time. Such
an approach yields inflated probability estimates, stemming from an implicitly
made assumption that recommendations are implemented instantaneously. We
propose using longitudinal data to alleviate such issues in two ways. First, we
use past outcome probabilities as features in the present. Use of such past
probabilities ties historical behavior to the present, allowing for more
information to be taken into account when making initial probability estimates
and subsequently performing inverse classification. Secondly, following inverse
classification application, optimized instances' unchangeable features
(e.g.,~age) are updated using values from the next longitudinal time period.
Optimized test instance probabilities are then reassessed. Updating the
unchangeable features in this manner reflects the notion that improvements in
outcome likelihood, which result from following the inverse classification
recommendations, do not materialize instantaneously. As our experiments
demonstrate, more realistic estimates of probability can be obtained by
factoring in such considerations.
