Realistic risk-mitigating recommendations via inverse classification

TL;DR

This paper enhances inverse classification by incorporating longitudinal data and updating unchangeable features over time, leading to more realistic risk-mitigating recommendations and probability estimates.

Contribution

It introduces a novel approach using longitudinal data and feature updates to improve the realism and accuracy of inverse classification recommendations.

Findings

More accurate probability estimates with longitudinal data.

Improved risk mitigation recommendations over time.

Enhanced modeling of real-world implementation delays.

Abstract

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.