An Evolutionary-Based Approach to Learning Multiple Decision Models from Underrepresented Data

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The use of multiple Decision Models (DMs) enables to enhance the accuracy in decisions and at the same time allows users to evaluate the confidence in decision making. In this paper we explore the ability of multiple DMs to learn from a small amount of verified data. This becomes important when data samples are difficult to collect and verify. We propose an evolutionary-based approach to solving this problem. The proposed technique is examined on a few clinical problems presented by a small amount of data.
5 pages, 3 figures, 2 tables, The 4 th International Conference on Natural Computation (ICNC'08)

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Artificial Intelligence, Neural and Evolutionary Computing

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http://salesiana.dossiersoluciones.com/handle/123456789/161615

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0805.3800
 http://arxiv.org/abs/0805.3800

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arXiv