Generalized Inverse Classification

Michael Lash; Qihang Lin; Nick Street; Jennifer Robinson; Jeffrey Ohlmann

doi:10.1137/1.9781611974973.19

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Conference proceeding

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Generalized Inverse Classification

Michael Lash, Qihang Lin, Nick Street, Jennifer Robinson and Jeffrey Ohlmann

Proceedings of the 2017 SIAM International Conference on Data Mining (SDM), pp.162-170

01/12/2017

DOI: 10.1137/1.9781611974973.19

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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.

Classification

Classifiers

Generalized inverse

Heuristic methods

Sensitivity analysis

Details

Title: Subtitle: Generalized Inverse Classification
Creators: Michael Lash - University of Iowa
Qihang Lin - University of Iowa
Nick Street
Jennifer Robinson
Jeffrey Ohlmann - University of Iowa
Resource Type: Conference proceeding
Publication Details: Proceedings of the 2017 SIAM International Conference on Data Mining (SDM), pp.162-170
DOI: 10.1137/1.9781611974973.19
Publisher: Society for Industrial and Applied Mathematics
Language: English
Date published: 01/12/2017
Academic Unit: Bus Admin College; Epidemiology; Nursing; Fraternal Order of Eagles Diabetes Research Center; Computer Science; Business Analytics
Record Identifier: 9984363593302771

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