Kadlec, P. and Gabrys, B., 2009. Architecture for development of adaptive on-line prediction models. Memetic Computing, 1 (4), pp. 241-269.
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Official URL: http://www.springerlink.com/content/x5tv03g4721247...
This work presents an architecture for the development of on-line prediction models. The archi- tecture defines an unied modular environment based on three concepts frome machine learning, these are: (i) ensemble methods; (ii) local learning; and (iii) meta learning. The three concepts are organised in a three layer hierarchy within the architecture. For the actual prediction any data-driven predictive method such as articial neural network, support vector machines, etc. can be implemented and plugged in. Additionally to the predictive methods, data pre-processing methods are also managed and can also be implemented as plug- ins. Models developed according to the architecture can be trained and operated in different modes. As for the training, the architecture supports the building of ini- tial models based on a batch of training data, but if this data is not available the models can also be trained in incremental mode. In a scenario where correct target values are (occasionally) available during the run-time, the architecture supports life-long learning by providing several adaptation mechanisms across the three hierar- chical levels. In order to demonstrate its practicality, we show how the issues of current soft sensor development and maintenance can be effectively dealt with by using the architecture as a construction plan for automated development and maintenance of the soft sensors.
|Uncontrolled Keywords:||Adaptive systems; Local learning; Meta learning; Ensemble methods; Industrial applications; Soft sensors; Life-long learning|
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
Generalities > Computer Science and Informatics
Technology > Engineering > General Engineering
|Group:||School of Design, Engineering & Computing > Smart Technology Research Centre|
|Deposited By:||Professor Bogdan Gabrys|
|Deposited On:||02 Feb 2009 18:35|
|Last Modified:||07 Mar 2013 15:06|
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