Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/331

 Title: Experiments with two Approaches for Tracking Drifting Concepts Authors: Koychev, Ivan Keywords: Machine LearningConcept DriftForgetting Models Issue Date: 2007 Publisher: Institute of Mathematics and Informatics Bulgarian Academy of Sciences Citation: Serdica Journal of Computing, Vol. 1, No 1, (2007), 27p-44p Abstract: This paper addresses the task of learning classifiers from streams of labelled data. In this case we can face the problem that the underlying concepts can change over time. The paper studies two mechanisms developed for dealing with changing concepts. Both are based on the time window idea. The first one forgets gradually, by assigning to the examples weight that gradually decreases over time. The second one uses a statistical test to detect changes in concept and then optimizes the size of the time window, aiming to maximise the classification accuracy on the new examples. Both methods are general in nature and can be used with any learning algorithm. The objectives of the conducted experiments were to compare the mechanisms and explore whether they can be combined to achieve a synergetic e ect. Results from experiments with three basic learning algorithms (kNN, ID3 and NBC) using four datasets are reported and discussed. URI: http://hdl.handle.net/10525/331 ISSN: 1312-6555 Appears in Collections: Volume 1 Number 1

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