A fuzzy kernel c-means clustering model for handling concept drift in regression

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Conference Proceeding
IEEE International Conference on Fuzzy Systems, 2017
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© 2017 IEEE. Concept drift, given the huge volume of high-speed data streams, requires traditional machine learning models to be self-adaptive. Techniques to handle drift are especially needed in regression cases for a wide range of applications in the real world. There is, however, a shortage of research on drift adaptation for regression cases in the literature. One of the main obstacles to further research is the resulting model complexity when regression methods and drift handling techniques are combined. This paper proposes a self-adaptive algorithm, based on a fuzzy kernel c-means clustering approach and a lazy learning algorithm, called FKLL, to handle drift in regression learning. Using FKLL, drift adaptation first updates the learning set using lazy learning, then fuzzy kernel c-means clustering is used to determine the most relevant learning set. Experiments show that the FKLL algorithm is better able to respond to drift as soon as the learning sets are updated, and is also suitable for dealing with reoccurring drift, when compared to the original lazy learning algorithm and other state-of-the-art regression methods.
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