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Most recent machine learning research focuses on developing new classifiersfor the sake of improving classification accuracy. With many well-performingstate-of-the-art classifiers available, there is a growing need forunderstanding interpretability of a classifier necessitated by practicalpurposes such as to find the best diet recommendation for a diabetes patient.Inverse classification is a post modeling process to find changes in inputfeatures of samples to alter the initially predicted class. It is useful inmany business applications to determine how to adjust a sample input data suchthat the classifier predicts it to be in a desired class. In real worldapplications, a budget on perturbations of samples corresponding to customersor patients is usually considered, and in this setting, the number ofsuccessfully perturbed samples is key to increase benefits. In this study, wepropose a new framework to solve inverse classification that maximizes thenumber of perturbed samples subject to a per-feature-budget limits andfavorable classification classes of the perturbed samples. We design algorithmsto solve this optimization problem based on gradient methods, stochasticprocesses, Lagrangian relaxations, and the Gumbel trick. In experiments, wefind that our algorithms based on stochastic processes exhibit an excellentperformance in different budget settings and they scale well.

Inverse Classification with Limited Budget and Maximum Number of Perturbed Samples