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Component-Based Decision Trees: Empirical Testing on Data Sets of Account Holders in the Montenegrin Capital Market

Component-Based Decision Trees: Empirical Testing on Data Sets of Account Holders in the Montenegrin Capital Market
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Author(s): Ljiljana Kašćelan (Faculty of Economics, University of Montenegro, Podgorica, Montenegro) and Vladimir Kašćelan (Faculty of Economics, University of Montenegro, Podgorica, Montenegro)
Copyright: 2015
Volume: 6
Issue: 4
Pages: 18
Source title: International Journal of Operations Research and Information Systems (IJORIS)
Editor(s)-in-Chief: John Wang (Montclair State University, USA)
DOI: 10.4018/IJORIS.2015100101

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Abstract

Popular decision tree (DT) algorithms such as ID3, C4.5, CART, CHAID and QUEST may have different results using same data set. They consist of components which have similar functionalities. These components implemented on different ways and they have different performance. The best way to get an optimal DT for a data set is one that use component-based design, which enables user to intelligently select in advance implemented components well suited to specific data set. In this article the authors proposed component-based design of the optimal DT for classification of securities account holders. Research results showed that the optimal algorithm is not one of the original DT algorithms. This fact confirms that the component design provided algorithms with better performance than the original ones. Also, the authors found how the specificities of the data influence the DT components performance. Obtained results of classification can be useful to the future investors in the Montenegrin capital market.

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