1998
DOI: 10.1002/(sici)1099-1174(199809)7:3<173::aid-isaf147>3.0.co;2-5
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Using a genetic algorithm-based classifier system for modeling auditor decision behavior in a fraud setting
Abstract: This paper addresses a classification problem involving the decisions of Defense Contractor Audit Agency (DCAA) auditors when they are estimating the likelihood of fraud by contractors developing bids for government contracts. The objective of the study is to investigate if this decision involves non‐algebraic processes associated with a posited simultaneous decision model or algebraic processes posited by sequential decision processes. We propose that in classification decision models involving simultaneous p…
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Cited by 25 publications
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“…Spathis (2003) reports in-sample (i.e., training) overall classification accuracies equal to 75% and 78%, similar to Ruiz- Barbadillo et al (2004) that report in-sample overall classification accuracy equal to79.7%. The results are also similar in studies that used re-sampling techniques such as Spathis et al (2002Spathis et al ( , 2003, or holdout samples such as Welch et al (1998) to test the models and report overall classification accuracies between 71.79% and 86.91%.…”
Section: Table
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confidence: 71%
