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Title: |
Vote prediction by iterative domain knowledge and attribute elimination |
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Author: |
Anthony Scime, Gregg R. Murray 
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Address: |
Computer Science, SUNY College at Brockport, 350 New Campus Drive, Brockport, NY 14420-2933, USA. ' Political Science, SUNY College at Brockport, 350 New Campus Drive, Brockport, NY 14420, USA |
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Journal: |
International Journal of Business Intelligence and Data Mining 2007 - Vol. 2, No.2 pp. 160 - 176 |
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Abstract: |
Data mining the American National Election Study (ANES), a rich but disparate source of information about Americans' vote choices, is the focus of this research. Specifically, we use data mining classification to construct a decision tree to select important predictors of the vote from the more than 900 items that compose the ANES. We use an iterative domain expert and data mining process to identify a limited number of survey questions intended to predict for which party an individual will vote in a presidential election or whether that individual will vote at all. |
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Keywords: |
classification; data mining; dimensionality reduction; domain expert; domain knowledge; elections; information gain; political science; voting intentions; vote prediction; presidential election; political party choice. |
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DOI: |
10.1504/IJBIDM.2007.013935 |
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