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Evolving on-line prediction model dealing with industrial data sets.

Kadlec, P. and Gabrys, B., 2009. Evolving on-line prediction model dealing with industrial data sets. In: 2009 IEEE Workshop on Evolving and Self-Developing Intelligent Systems Proceedings. Nashville: IEEE, 24-31.

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Kadlec_Gabrys_SSCI_ESDIS2009.pdf - Published Version



In this work we present an instance of an architecture for the development of robust evolving predictive models. The architecture provides a conceptual framework for the development of such models while at the same time it provides mechanisms for the minimisation of effort needed for the development and maintenance of the models. These mechanisms deal with the model and parameter selection, model training, validation and adaptation. Another challenge for the proposed instance is to deal with an industrial data set containing several issues like missing data, outliers, drifting data, etc. This fact calls for high robustness of the deployed models. The success of the models lays in the goal oriented application of several concepts like ensemble building, local learning, parameter cross-validation which are provided by the architecture and exploited by the discussed instance.

Item Type:Book Section
Series Name:2009 IEEE Symposium Series on Computational Intelligence
Group:Faculty of Science & Technology
ID Code:9530
Deposited By: Professor Bogdan Gabrys LEFT
Deposited On:02 Feb 2009 20:28
Last Modified:14 Mar 2022 13:21


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