IMI-BAS
 

BulDML at Institute of Mathematics and Informatics >
IMI >
IMI Periodicals >
Serdica Journal of Computing >
2007 >
Volume 1 Number 1 >

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 Learning
Concept Drift
Forgetting 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

Files in This Item:

File Description SizeFormat
sjc008-vol1-num1-2007.pdf189.84 kBAdobe PDFView/Open

 



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! DSpace Software Copyright © 2002-2009  The DSpace Foundation - Feedback