Abstract
ARES System is an application dedicated to data analysis supported by Rough Set theory. Currently the system is expanded by such approaches as Emerging Patterns and Support Vector Machine. A unique feature of ARES System is applying credibility coefficients to identify improper objects within information systems. The credibility coefficient is a measure, which attempts to assess a degree of typicality of each object in respect to the rest of information system. The paper presents a concept of credibility coefficients in context of hybrid artificial intelligence systems combined on ARES System platform. Ordinal credibility coefficient supports aggregation of number incomparable credibility coefficients based on different approaches.
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Podraza, R. (2009). Credibility Coefficients in Hybrid Artificial Intelligence Systems. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_22
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DOI: https://doi.org/10.1007/978-3-642-02319-4_22
Publisher Name: Springer, Berlin, Heidelberg
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