Abstract
Voting Advice Application (VAA) is a web application that recommends a candidate or a party to a voter. From an online questionnaire, which voters and candidates are called to answer, the VAA proposes to each individual voter the candidate who replied like him/her. It is very important the voters to reply in all questions of the questionnaire, because every question has its meaning and is responding to the political position of a each party. Missing values might mislead the VAA and impede it to have complete knowledge about the voter, as a result to offer him/her the wrong candidate. In this paper we quantitatively investigate the effect of missing values in VAAs by examining the impact of the number of missing values to different methods of voting prediction. For our experiment we have used the data obtained from the May parliamentary elections in Greece in 2012. The corresponding dataset is made freely available to other researchers working in the areas of VAA and recommender systems through the Web.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baka, A., Figgou, L., Triga, V.: ‘Neither agree, nor disagree’: a critical analysis of the middle answer category in Voting Advice Applications. Int. J. Electronic Governance 5(3/4), 244–263 (2012)
Cedroni, L., Diego, G. (eds.): Voting Advice Applications in Europe: The State of the Art. ScriptaWeb, Napoli (2010)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann (2011)
Katakis, I., Tsapatsoulis, N., Triga, V., Tziouvas, C., Mendez, F.: Clustering Online Poll Data: Towards a Voting Assistance System. In: Semantic and Social Media Adaptation and Personalization (SMAP 2012), pp. 54–59. IEEE Press (2012)
Ladner, A., Pianzola, J.: Do Voting Advice Applications Have an Effect on Electoral Participation and Voter Turnout? Evidence from the 2007 Swiss Federal Elections. In: Tambouris, E., Macintosh, A., Glassey, O. (eds.) ePart 2010. LNCS, vol. 6229, pp. 211–224. Springer, Heidelberg (2010)
Mendez, F.: Matching voters with political parties and candidates: An empirical test of four algorithms. International Journal of ElectronicGovernance (2012)
Pianzola, J., Trechsel, A.H., Schwerdt, G., Vassil, K., Alvarez, R.M.: The Effect of Voting Advice Applications (VAAs) on Political Preferences. Evidence from a Randomized Field Experiment. In: Annual Meeting of the American Political Science Association (2012), Available at SSRN: http://ssrn.com/abstract=2108095
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook, pp. 39–71. Springer, Heidelberg (2011)
Tsapatsoulis, N., Georgiou, O.: Investigating the Scalability of Algorithms, the Role of Similarity Metric and the List of Suggested Items Construction Scheme in Recommender Systems. International Journal on Artificial Intelligence Tools 21(4), 12–40 (2012)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn., pp. 3–9. Morgan Kaufmann (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Agathokleous, M., Tsapatsoulis, N., Katakis, I. (2013). On the Quantification of Missing Value Impact on Voting Advice Applications. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_51
Download citation
DOI: https://doi.org/10.1007/978-3-642-41013-0_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41012-3
Online ISBN: 978-3-642-41013-0
eBook Packages: Computer ScienceComputer Science (R0)