Abstract
The authors have implemented a complex adaptive simulation of an agent-based exchange to estimate the relative importance of attributes in a data set. This simulation uses an individual, transaction-based voting mechanism to help the system estimate the importance of each variable at the system/aggregate level. Two variations of information gain – one using entropy and one using similarity – were used to demonstrate that the resulting estimates can be computed using a smaller subset of the data and greater accommodation for missing and erroneous data than traditional methods.
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Eichelberger, C.N., Hadžikadić, M. (2006). Complex Adaptive Systems: Using a Free-Market Simulation to Estimate Attribute Relevance. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_74
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DOI: https://doi.org/10.1007/11875604_74
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