Generalized Inverse Classification

TL;DR

This paper introduces a generalized inverse classification framework that is flexible across classifiers, considers actionable feature changes with costs, and accounts for indirect feature effects, validated on real datasets.

Contribution

It presents a novel, classifier-agnostic inverse classification framework with cost-aware and indirect feature change modeling, along with heuristic and sensitivity analysis methods.

Findings

Validated on real-world datasets showing effectiveness

Demonstrated benefits over traditional inverse classification methods

Proposed methods outperform baseline approaches

Abstract

Inverse classification is the process of perturbing an instance in a meaningful way such that it is more likely to conform to a specific class. Historical methods that address such a problem are often framed to leverage only a single classifier, or specific set of classifiers. These works are often accompanied by naive assumptions. In this work we propose generalized inverse classification (GIC), which avoids restricting the classification model that can be used. We incorporate this formulation into a refined framework in which GIC takes place. Under this framework, GIC operates on features that are immediately actionable. Each change incurs an individual cost, either linear or non-linear. Such changes are subjected to occur within a specified level of cumulative change (budget). Furthermore, our framework incorporates the estimation of features that change as a consequence of direct actions taken (indirectly changeable features). To solve such a problem, we propose three real-valued heuristic-based methods and two sensitivity analysis-based comparison methods, each of which is evaluated on two freely available real-world datasets. Our results demonstrate the validity and benefits of our formulation, framework, and methods.