Complexity-driven Evolution of Meta-agents for Classification of Medical Data

V. Podgorelec (Slovenia)


classification, machine learning, complexity, metaagents, medical data


We study the possibility of constructing decision graphs with the help of several agents. We present a two-leveled evolutionary algorithm for the induction of decision graphs and describe the principle of classification based on the decision graphs. Several agents are used to construct the decision graphs. They are constructed and evolved with the help of automatic programming and evaluated with a universal complexity measure. The developed model is used for the classification of patients with mitral valve prolapse syndrome.

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