Edwin Lughofer, Eva Weigl, Wolfgang Heidl, Christian Eitzinger, Thomas Radauer,
"Fast and Economic Integration of New Classes On the Fly in Evolving Fuzzy Classifiers using Class Decomposition"
: Proceedings of the International FUZZ-IEEE Conference 2015, Serie FUZZ-IEEE 2015, 2015
Original Titel:
Fast and Economic Integration of New Classes On the Fly in Evolving Fuzzy Classifiers using Class Decomposition
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the International FUZZ-IEEE Conference 2015
Original Kurzfassung:
In this paper, we propose a fast and economic
strategy for the integration of new classes on the fly into evolving fuzzy classifiers (EFC) during data stream mining processes. Fastness addresses the assurance that a newly arising class in the stream can be integrated in a way such that the classifier is able to correctly return the new class after receiving only a few training samples of it. Economic means that the classifier update cycles are decreased to a minimum amount of time, as these require operator?s feedback for obtaining the ground truth labels, which are usually costly to obtain. The former is achieved by a class-decomposition approach, which splits up
multi-class classification problems into several less imbalanced and less complex binary sub-problems. The latter is achieved by a single-pass active learning selection scheme which selects the most informative samples based on sample-wise criteria. The approach is compared with conventional single model architecture for EFC (EFC-SM) based on two data streams from a real-world
application in the field of surface inspection. The comparison
shows that the class decomposition approach can significantly
reduce the delay of class integration, and this with a lower # of samples used for model updates than EFC-SM.