Vote prediction by iterative domain knowledge and attribute elimination

Anthony Scime, Gregg R. Murray

Research output: Contribution to journalArticle

12 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)160-176
Number of pages17
JournalInternational Journal of Business Intelligence and Data Mining
Volume2
Issue number2
DOIs
StatePublished - 2007
Externally publishedYes

Keywords

  • Classification
  • Data mining
  • Dimensionality reduction
  • Domain expert
  • Domain knowledge
  • Elections
  • Information gain
  • Political science
  • Prediction
  • Voting

ASJC Scopus subject areas

  • Management Information Systems
  • Statistics, Probability and Uncertainty
  • Information Systems and Management

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