Research Note—Discriminant Analysis with Strategically Manipulated Data
Juheng Zhang, Haldun Aytug, Gary J. Koehler
Information Systems Research · 2014 · Volume 25 · Issue 3
Abstract
We study the problem where a decision maker uses a linear classifier over attribute values (e.g., age, income, etc.) to classify agents into classes (e.g., creditworthy or not). Sometimes the attribute values are altered and/or hidden by agents to obtain a favorable but undeserved classification. Our main goal is to develop methods to thwart agents from hiding or distorting attribute values to obtain a favorable but incorrect classification. Intentionally altered attributes to obtain strategic goals have been studied. In this paper we develop methods that handle strategic hiding (i.e., nondisclosure) and then merge them with methods to thwart strategic distortion in the context of classification.
Keywords
classification, support vector machines, data imputation, missing values, adversarial learning, strategically hidden information
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