Edwin Lughofer,
"Flexible Evolving Fuzzy Inference Systems from Data Streams (FLEXFIS++)"
, in Moamar Sayed-Mouchaweh and Edwin Lughofer: Learning in Non-Stationary Environments: Methods and Applications, Springer, New York, Seite(n) 205-246, 2012
Original Titel:
Flexible Evolving Fuzzy Inference Systems from Data Streams (FLEXFIS++)
Sprache des Titels:
Englisch
Original Buchtitel:
Learning in Non-Stationary Environments: Methods and Applications
Original Kurzfassung:
Data streams are usually characterized by an ordered sequence of samples
recorded and loaded on-line with a certain frequency arriving continuously over
time. Extracting models from such type of data within a reasonable on-line computational
performance can be only achieved by a training procedure which is able to
incrementally build up the models, ideally in a single-pass fashion (not using any
prior samples). This chapter deals with data-driven design of fuzzy systems which
are able to handle sample-wise loaded data within a streaming context. These are
called Flexible Evolving Fuzzy Inference Systems (FLEXFIS) as they may permanently
change their structures and parameters with newly recorded data, achieving
maximal flexibility according to new operating conditions, dynamic system behaviors
or exceptional occurrences. We are explaining how to deal with parameter
adaptation and structure evolution on demand for regression as well as classification
problems. In the second part of the chapter, several key extensions of the FLEXFIS
family will be described (leading to the FLEXFIS++ and FLEXFIS-Class++ variants),
including concepts for on-line rule merging, dealing with drifts and reducing the curse of dimensionality as well as interpretability considerations and reliability in model predictions. Successful applications of the FLEXFIS family are summarized in a separate section. An extensive evaluation of the proposed methods and
techniques will be demonstrated in a separate chapter (Chapter 14), when dealing with the application of flexible fuzzy systems in on-line quality control systems.
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