Davy Sannen, Edwin Lughofer, Hendrik van Brussel,
"Increasing Online Classification Performance Using Incremental Classifier Fusion"
: Proceedings of the International Conference on Adaptive and Intelligent Systems (ICAIS) 2009, IEEE press, Seite(n) 101-107, 2009
Original Titel:
Increasing Online Classification Performance Using Incremental Classifier Fusion
Sprache des Titels:
Deutsch
Original Buchtitel:
Proceedings of the International Conference on Adaptive and Intelligent Systems (ICAIS) 2009
Original Kurzfassung:
To process the large amounts of data industrial systems are producing nowadays, machine learning techniques
have shown their usefulness
in many applications. As the amounts of data being generated are getting huge, the need for machine learning
methods which can deal with them in an appropriate way -- i.e. methods which can be adapted incrementally -- becomes
very important. Ensembles of classifiers have been shown to be able to improve the predictive accuracy as well as
the robustness of single classification methods.
In this work novel incremental variants of several well-known classifier fusion methods Fuzzy Integral,
Decision Templates, Dempster-Shafer Combination and Discounted Dempster-Shafer Combination)
are presented. Furthermore,
a novel incremental classifier fusion method called Incremental Direct Cluster-based fusion will be introduced,
which exploits an evolving clustering approach. A flexible and interactive framework for on-line learning will be introduced,
in which the ensemble (classifier fusion) methods are adapted incrementally in a sample-wise manner together with their base classifiers.
The performance of this framework and the proposed incremental classifiers fusion methods therein are evaluated on five
real-world visual quality inspection tasks, captured on-line from an industrial CD imprint production process.