When it comes to application of computational learning techniques in
practical scenarios, like for example adaptive inferential control, it is often difficult
to apply the state-of-the-art techniques in a straight forward manner and
usually some effort has to be dedicated to tuning either the data, in a form of
data pre-processing, or the modelling techniques, in form of optimal parameter
search or modification of the training algorithm. In this work we present a robust
approach to on-line predictive modelling which is focusing on dealing with
challenges like noisy data, data outliers and in particular drifting data which are
often present in industrial data sets. The approach is based on the local learning
approach, where models of limited complexity focus on partitions of the input
space and on an ensemble building technique which combines the predictions of
the particular local models into the final predicted value. Furthermore, the technique
provides the means for on-line adaptation and can thus be deployed in a
dynamic environment which is demonstrated in this work in terms of an application
of the presented approach to a raw industrial data set exhibiting drifting data,
outliers, missing values and measurement noise
