Effects of change propagation resulting from adaptive preprocessing in multicomponent predictive systems.

Salvador, M. M., Budka, M. and Gabrys, B., 2016. Effects of change propagation resulting from adaptive preprocessing in multicomponent predictive systems. Procedia Computer Science, 96, pp. 713-722. (In Press)

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DOI: 10.1016/j.procs.2016.08.255

Abstract

Predictive modelling is a complex process that requires a number of steps to transform raw data into predictions. Preprocessing of the input data is a key step in such process, and the selection of proper preprocessing methods is often a labour intensive task. Such methods are usually trained offline and their parameters remain fixed during the whole model deployment lifetime. However, preprocessing of non-stationary data streams is more challenging since the lack of adaptation of such preprocessing methods may degrade system performance. In addition, dependencies between different predictive system components make the adaptation process more challenging. In this paper we discuss the effects of change propagation resulting from using adaptive preprocessing in a Multicomponent Predictive System (MCPS). To highlight various issues we present four scenarios with different levels of adaptation. A number of experiments have been performed with a range of datasets to compare the prediction error in all four scenarios. Results show that well managed adaptation considerably improves the prediction performance. However, the model can become inconsistent if adaptation in one component is not correctly propagated throughout the rest of system components. Sometimes, such inconsistency may not cause an obvious deterioration in the system performance, therefore being difficult to detect. In some other cases it may even lead to a system failure as was observed in our experiments.

Item Type:Article
ISSN:1877-0509
Additional Information:Available online 4 September 2016
Uncontrolled Keywords:Adaptive preprocessing; data streams; incremental learning
Subjects:UNSPECIFIED
Group:Faculty of Science & Technology
ID Code:24674
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:21 Sep 2016 14:31
Last Modified:21 Sep 2016 14:31

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