Finding Significant Factors on World Ranking of e-Governments by Feature Selection Methods over KPIs
Computing significant factors quantitatively is an imperative task in understanding the underlying reasons that contribute to a final outcome. In this paper, a case of e-Government ranking is studied by attempting to find the significance of each KPIs which leads to resultant rank of a country. Significant factors in this context are inferred as some degrees of relations between the input variables (which are the KPIs in this case) and the final outcome (the rank). In the past, significant factors were either acquired as first-hand information via direct questioning from users’ satisfaction survey or qualitative inference; typical question is ‘You are satisfied with a particular e-Government service’ by applying a multi-level Likert scale. Respondents answered by choosing one of the following: Strongly agree, Agree, Neutral, Disagree, and Strongly Disagree. The replies are then counted and studied using traditional statistical methods. In this paper, an alternative method by feature selection in data mining is proposed which computes quantitatively the relative importance of each KPI with respective to the predicted class, the rank. The main advantage of feature selection by data mining (FSDM) method is that it considers the cross-dependencies of the variables and how they contribute as a whole predictive model to a particular predicted outcome. In contrast, classical significant factor analysis such as correlogram tells only the strength of correlation between an individual pair of factor and outcome. Another advantage of using data mining method over simple statistic is that the inferred predictive model could be used as a predictor and/or what-if decision simulator; given some values of KPIs a corresponding rank could be guesstimated. A case study of computing significant factors in terms of KPIs that lead to the world rank in from the data of UN e-Government Survey 2010, is presented.
Keywordse-Government Ranking Analytics Feature Selection Data Mining Principle of Component Analysis
Unable to display preview. Download preview PDF.
- 1.Bender, P.: Mathematical Modeling of trie 20/80 Rule: Theory and Practice. Journal of Business Logistics 2(2), 139–157 (1981)Google Scholar
- 6.Steyaert, J.: Measuring the Performance of Electronic Government Services. Information and Management (41), 369–375 (2004)Google Scholar
- 7.Hall, M.A.: Correlation-based Feature Subset Selection for Machine Learning. University of Waikato, PhD Thesis, Hamilton, New Zealand (1998)Google Scholar
- 8.Bidgoli, A.M., Parsa, M.N.: A Hybrid Feature Selection by Resampling, Chi-squared and Consistency Evaluation Techniques. World Academy of Science, Engineering and Technology 68, 276–285 (2012)Google Scholar
- 9.Mukras, R., Wiratunga, N., Lothian, R., Chakraborti, S., Harper, D.: Information gain feature selection for ordinal text classification using probability Re-distribution. In: Proceedings of the Textlink workshop at IJCAI 2007, pp. 1–10 (2007)Google Scholar
- 10.Robnik-Sikonja, M., Kononenko, I.: An adaptation of Relief for attribute estimation in regression. In: Fourteenth International Conference on Machine Learning, pp. 296–304 (1997)Google Scholar
- 13.Jolliffe, I.T.: Principal Component Analysis. Springer Series in Statistics, 2nd edn. (2002). ISBN 0-387-95Google Scholar