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Author Yang, Yanjuan ♦ Mannino, Michael
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2012
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Screening policy ♦ Artificial deception ♦ Boosted deception ♦ Data generation model ♦ Deception ♦ Natural deception ♦ Noise
Abstract Developing policies to screen documents for deception is often hampered by the cost of data collection and the inability to evaluate policy alternatives due to lack of data. To lower data collection costs and increase the amount of data, artificially generated deception data can be used, but the impact of using artificially generated deception data is not well understood. This article studies the impact of artificially generated deception on document screening policies. The deception and truth data were collected from financial aid applications, a document-centric area with limited resources for screening. Real deception was augmented with artificial data generated by noise and deception generation models. Using the real data and artificially generated data, we designed an innovative experiment with deception type and deception rate as factors, and harmonic mean and cost as outcome variables. We used two budget models (fixed and variable) typically employed by financial aid offices to measure the cost of noncompliance in financial aid applications. The analysis included an evaluation of a common policy for deception screening using both fixed and varying screening rates. The results of the experiment provided evidence of similar performance of screening policy with real and artificial deception, suggesting the possibility of using artificially generated deception to reduce the costs associated with obtaining training data.
ISSN 19361955
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2012-08-01
Publisher Place New York
e-ISSN 19361963
Journal Journal of Data and Information Quality (JDIQ)
Volume Number 3
Issue Number 3
Page Count 25
Starting Page 1
Ending Page 25

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Source: ACM Digital Library