Vote prediction by iterative domain knowledge and attribute elimination

International Journal of Business Intelligence and Data Mining

Volumn 2, Issue 2, 2007, Pages 160-176

  Scime, Anthony ^a   Murray, Gregg R ^a  

^a STATE UNIVERSITY OF NEW YORK   (United States)

Author keywords

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

Indexed keywords

EID: 34250885168     PISSN: 17438187     EISSN: 17438195     Source Type: Journal    
DOI: 10.1504/IJBIDM.2007.013935     Document Type: Article

References (28)

1. Abramson, P.R., Aldrich, J.H. and Rohde, D.W. (2003) Change and Continuity in the 2000 and 2002 Elections, Congressional Quarterly Press, Washington DC.
2. Ali, S. and Smith, K.A. (2005) Kernel width selection for SVM classification: a meta-learning approach', International Journal of Data Warehousing and Mining, Vol. 1, No. 4, pp.78-97.
3. American National Election Studies (ANES) (2005) Center for Political Studies, University of Michigan, Ann Arbor, MI.
4. Anand, S.S., Bell, D.A. and Hughes, J.G. (1995) 'The role of domain knowledge in data mining', Proceedings of the Fourth International Conference on Information and Knowledge Management, Baltimore, MD, pp.37-43.
5. Andoh-Baidoo, F.K. and Osei-Bryson, K. (2007) 'Exploring the characteristics of internet security breaches that impact the market value of breached firms', Expert Systems with Applications, Vol. 32, No. 3, pp.703-725.
6. Ankerst, M., Ester, M. and Kriegel, H. (2000) 'Towards an effective cooperation of the user and the computer for classification', Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, pp. 179-188.
7. Bagui, S. (2006) 'An approach to mining crime patterns', International Journal of Data Warehousing and Mining, Vol. 2, No. 1, pp.50-80.
8. Burton, M.J. and Shea, D.M. (2003) Campaign Mode: Strategic Vision in Congressional Elections, Rowman and Littlefield, New York.
9. Deshpande, M. and Karypis, G. (2002) 'Using conjunction of attribute values for classification', Proceedings of the Eleventh International Conference on Information and Knowledge Management, McLean, VA, pp.356-364.
10. DuMouchel, W. and Pregibon, D. (2001) 'Empirical Bayes screening for multi-item associations', Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, pp.67-76.
11. Edsall, T.B. (2006) 'Democrats' data mining stirs an intraparty battle', The Washington Post, March 8, p.A01.
12. Fiorina, M.P. (1981) Retrospective Voting in American National Elections, Yale University Press, New Haven, CT.
13. Freitas, A.A. (2000) 'Understanding the crucial differences between classification and discovery of association rules - a position paper', ACM SIGKDD Explorations Newsletter, Vol. 2, No. 1, pp.65-69.
14. Fu, X. and Wang, L. (2005) 'Data dimensionality reduction with application to improving classification performance and explaining concepts of data sets', International Journal of Business Intelligence and Data Mining, Vol. 1, No. 1, pp.65-87.
15. Han, J. and Kamber, M. (2001) Data Mining: Concepts and Techniques, Morgan Kaufmann, Boston, MA.
16. Hofmann, M. and Tierney, B. (2003) 'The involvement of human resources in large scale data mining projects', Proceedings of the 1st International Symposium on Information and Communication Technologies, Dublin, Ireland, pp. 103-109.
17. Hu, X., Song, I-Y., Han, H., Yoo, I., Prestrud, A.A., Brennan, M.F. and Brooks, A.D. (2005) 'Temporal rule induction for clinical outcome analysis', International Journal Business Intelligence and Data Mining, Vol. 1, No. 1, pp. 122-136.
18. Jaroszewicz, S. and Simovici, D.A. (2004) 'Interestingness of frequent itemsets using Bayesian networks as background knowledge', Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, pp. 178-186.
19. Jeong, M. and Lee, D. (2005) 'Improving classification accuracy of decision trees for different abstraction levels of data', International Journal of Data Warehousing and Mining, Vol. 1, No. 3, pp.1-14.
20. Lacy, D. and Burden, B.C. (1999) 'The vote-stealing and turnout effects of Ross Perot in the 1992 US presidential election', American Journal of Political Science, Vol. 43, No. 1, pp.233-255.
21. MacKuen, M.B., Erikson, R.S. and Stimson, J.A. (1992) 'Peasants or Bankers? the American electorate and the US economy', American Political Science Review, Vol. 86, No. 3, pp.597-611.
22. Nadeau, R. and Lewis-Beck, M.S. (2001) 'National economic voting in US presidential elections', Journal of Politics, Vol. 63, No. 1, pp. 159-181.
23. Niemi, R.G. and Weisberg, H.F. (2001) 'What determines the vote?' in Niemi, R.G. and Weisberg, H.F. (Eds.): Controversies in Voting Behavior, 4th ed., Congressional Quarterly Press, Washington DC, pp. 180-199.
24. Osei-Bryson, K. (2004) 'Evaluation of decision trees: a multicriteria approach', Computers and Operations Research, Vol. 31, No. 11, pp.1933-1945.
25. Padmanabhan, B. and Tuzhilin, A. (2000) 'Small is beautiful: discovering the minimal set of unexpected patterns', Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, pp.54-63.
26. Scholz, M. (2005) 'Sampling-based sequential subgroup mining', Proceedings' of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, pp.265-274.
27. Taft, M., Krishnan, R., Hornick, M., Mukhin, D., Tang, G. and Thomas, S. (2003) Oracle Data Mining Concepts, Oracle, Redwood City,, CA.
28. Witten, I.H. and Frank, E. (2005) Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed., Morgan Kaufmann, San Francisco, CA.