Research Note —Discriminant Analysis with Strategically Manipulated Data

Juheng Zhang (), Haldun Aytug () and Gary J. Koehler ()
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Juheng Zhang: Department of Operations and Information Systems, Manning School of Business, University of Massachusetts Lowell, Lowell, Massachusetts 01854
Haldun Aytug: Department of Information Systems and Operations Management, Warrington College of Business Administration, University of Florida, Gainesville, Florida 32611
Gary J. Koehler: Department of Information Systems and Operations Management, Warrington College of Business Administration, University of Florida, Gainesville, Florida 32611

Information Systems Research, 2014, vol. 25, issue 3, 654-662

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 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:25:y:2014:i:3:p:654-662

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