Detection of erroneous payments utilizing supervised and utilizing supervised and unsupervised data mining techniques
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Authors
Yanik, Todd E.
Subjects
Data Mining
Erroneous Payments
Logistic Regression
Hosmer Lemeshow Test
Classification and Regression Trees
Receiver Operator Characteristic curves
supervised and unsupervised modeling
Erroneous Payments
Logistic Regression
Hosmer Lemeshow Test
Classification and Regression Trees
Receiver Operator Characteristic curves
supervised and unsupervised modeling
Advisors
Buttrey, Samuel E.
Date of Issue
2004-09
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
In this thesis we develop a procedure for detecting erroneous payments in the Defense Finance Accounting Service, Internal Review's (DFAS IR) Knowledge Base Of Erroneous Payments (KBOEP), with the use of supervised (Logistic Regression) and unsupervised (Classification and Regression Trees (C & RT)) modeling algorithms. S-Plus software was used to construct a supervised model of vendor payment data using Logistic Regression, along with the Hosmer-Lemeshow Test, for testing the predictive ability of the model. The Clementine Data Mining software was used to construct both supervised and unsupervised model of vendor payment data using Logistic Regression and C & RT algorithms. The Logistic Regression algorithm, in Clementine, generated a model with predictive probabilities, which were compared against the C & RT algorithm. In addition to comparing the predictive probabilities, Receiver Operating Characteristic (ROC) curves were generated for both models to determine which model provided the best results for a Coincidence Matrix's True Positive, True Negative, False Positive and False Negative Fractions. The best modeling technique was C & RT and was given to DFAS IR to assist in reducing the manual record selection process currently being used. A recommended ruleset was provided, along with a detailed explanation of the algorithm selection process.
Type
Thesis
Description
Series/Report No
Department
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
xvii, 75 p. : ill. (some col.) ;
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.