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Determining the Efficacy of Data-Mining Methods in Predicting Gaming Ballot Outcomes

Oklahoma State University

Ercan Sirakaya

Texas A&M University

The purpose of this study is to test the efficacy of three popular data-mining methods (artificial neural networks, decision trees, and rough sets) by comparing and contrasting them using gambling ballot data that were collected for tourism policy purposes. Sixty unique prediction models were built for this comparative study. The findings of the study suggest that the rough-set algorithm was the best forecasting tool (among the three) with a cross-validation predictive accuracy of 83.8%, followed by artificial neural networks (79.4%) and decision trees (76.7%). Although the political utility of this study remains to be established, there is sufficient evidence to indicate the efficacy of rough sets in fore-casting gaming ballot outcomes. Policy makers, politicians, investors, and public service administrators can potentially use the results of these contemporary forecasting methods in their decision-making processes. Implications of the findings are discussed within the context of data mining and gambling literature.

Key Words: data mining • political forecasting • forecasting ballot outcomes • gambling ballots • decision trees • artificial neural networks • rough sets • k-fold cross-validations

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