IBLStreams: a system for instance-based classification and regression on data streams. Shaker, A. & Hüllermeier, E. Evolving Systems, 3(4):235–249, December, 2012. ![IBLStreams: a system for instance-based classification and regression on data streams [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This paper presents an approach to learning on data streams called IBLStreams. More specifically, we introduce the main methodological concepts underlying this approach and discuss its implementation under the MOA software framework. IBLStreams is an instance-based algorithm that can be applied to classification and regression problems. In comparison to model-based methods for learning on data streams, it is conceptually simple. Moreover, as an algorithm for learning in dynamically evolving environments, it has a number of desirable properties that are not, at least not as a whole, shared by currently existing alternatives. Our experimental validation provides evidence for its flexibility and ability to adapt to changes of the environment quickly, a point of utmost importance in the data stream context. At the same time, IBLStreams turns out to be competitive to state-of-the-art methods in terms of prediction accuracy. Moreover, due to its robustness, it is applicable to streams with different characteristics.
@article{shaker_iblstreams_2012,
title = {{IBLStreams}: a system for instance-based classification and regression on data streams},
volume = {3},
issn = {1868-6486},
shorttitle = {{IBLStreams}},
url = {https://doi.org/10.1007/s12530-012-9059-0},
doi = {10.1007/s12530-012-9059-0},
abstract = {This paper presents an approach to learning on data streams called IBLStreams. More specifically, we introduce the main methodological concepts underlying this approach and discuss its implementation under the MOA software framework. IBLStreams is an instance-based algorithm that can be applied to classification and regression problems. In comparison to model-based methods for learning on data streams, it is conceptually simple. Moreover, as an algorithm for learning in dynamically evolving environments, it has a number of desirable properties that are not, at least not as a whole, shared by currently existing alternatives. Our experimental validation provides evidence for its flexibility and ability to adapt to changes of the environment quickly, a point of utmost importance in the data stream context. At the same time, IBLStreams turns out to be competitive to state-of-the-art methods in terms of prediction accuracy. Moreover, due to its robustness, it is applicable to streams with different characteristics.},
language = {en},
number = {4},
urldate = {2022-03-20},
journal = {Evolving Systems},
author = {Shaker, Ammar and Hüllermeier, Eyke},
month = dec,
year = {2012},
pages = {235--249},
}
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