A1 Journal article (refereed)
Dynamic integration of classifiers for handling concept drift (2008)
Tsymbal, A., Pechenizkiy, M., Cunningham, P., & Puuronen, S. (2008). Dynamic integration of classifiers for handling concept drift. Information fusion, 9(1), 56-68. https://doi.org/10.1016/j.inffus.2006.11.002
JYU authors or editors
Publication details
All authors or editors: Tsymbal, Alexey; Pechenizkiy, Mykola; Cunningham, Padraig; Puuronen, Seppo
Journal or series: Information fusion
ISSN: 1566-2535
eISSN: 1872-6305
Publication year: 2008
Volume: 9
Issue number: 1
Pages range: 56-68
Publisher: Elsevier
Publication country: Netherlands
Publication language: English
DOI: https://doi.org/10.1016/j.inffus.2006.11.002
Publication open access: Not open
Publication channel open access:
Abstract
In the real world concepts are often not stable but change with time. A typical example of this in the biomedical context is antibiotic resistance, where pathogen sensitivity may change over time as new pathogen strains develop resistance to antibiotics that were previously effective. This problem, known as concept drift, complicates the task of learning a model from data and requires special approaches, different from commonly used techniques that treat arriving instances as equally important contributors to the final concept. The underlying data distribution may change as well, making previously built models useless. This is known as virtual concept drift. Both types of concept drifts make regular updates of the model necessary. Among the most popular and effective approaches to handle concept drift is ensemble learning, where a set of models built over different time periods is maintained and the best model is selected or the predictions of models are combined, usually according to their expertise level regarding the current concept. In this paper we propose the use of an ensemble integration technique that would help to better handle concept drift at an instance level. In dynamic integration of classifiers, each base classifier is given a weight proportional to its local accuracy with regard to the instance tested, and the best base classifier is selected, or the classifiers are integrated using weighted voting. Our experiments with synthetic data sets simulating abrupt and gradual concept drifts and with a real-world antibiotic resistance data set demonstrate that dynamic integration of classifiers built over small time intervals or fixed-sized data blocks can be significantly better than majority voting and weighted voting, which are currently the most commonly used integration techniques for handling concept drift with ensembles.
Keywords: machine learning; concepts (notions); environmental changes; modelling (representation); forecasts; models (objects); simulation
Free keywords: Machine learning; Changing environment; Concept drift; Ensemble learning; Dynamic integration of classifiers
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