Abstract
This paper describes Traffic Lights Panel (TLP) as a useful interpretation-oriented tool for clustering results, suitable for helping the domain experts to induce a conceptualization of the resulting profiles. Till now, the TLP is manually derived from the clustering results, but it has been well accepted by the domain experts of several real applications as a very helpful contribution to understand the classes’ meaning and improve reliable decision-making. Here, a proposal to automatically construction of TLP is presented trying to mimic the real process that the analyst performs. Two criteria based on different central trend statistics of the variables inside a class are introduced, tested with a real case study in Neurorehabilitation field and compared. Finally, uncertainty concerning TLP is analyzed; the annotated TLP (aTLP) is proposed to visualize uncertainty associated to the decisions derived from TLP, thus enhancing robustness of TLP as a supporting tool in decision-making.
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Gibert, K., Conti, D., Sànchez-Marrè, M. (2012). Decreasing Uncertainty When Interpreting Profiles through the Traffic Lights Panel. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 298. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31715-6_16
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DOI: https://doi.org/10.1007/978-3-642-31715-6_16
Publisher Name: Springer, Berlin, Heidelberg
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