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HICSS
2003
IEEE

A Two-Level Approach to Making Class Predictions

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A Two-Level Approach to Making Class Predictions
In this paper we propose a new two-level methodology for assessing countries’/companies’ economic/financial performance. The methodology is based on two major techniques of grouping data: cluster analysis and predictive classification models. First we use cluster analysis in terms of self-organizing maps to find possible clusters in data in terms of economic/financial performance. We then interpret the maps and define outcome values (classes) for each data row. Lastly we build classifiers using two different predictive models (multinomial logistic regression and decision trees) and compare the accuracy of these models. Our findings claim that the results of the two classification techniques are similar in terms of accuracy rate and class predictions. Furthermore, we focus our efforts on understanding the decision process corresponding to the two predictive models. Moreover, we claim that our methodology, if correctly implemented, extends the applicability of the self-organizing ma...
Adrian Costea, Tomas Eklund
Added 04 Jul 2010
Updated 04 Jul 2010
Type Conference
Year 2003
Where HICSS
Authors Adrian Costea, Tomas Eklund
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