Scalable Detection of Concept Drift: A Learning Technique Based on Support Vector Machines. Altendeitering, M. & Dübler, S. Procedia Manufacturing, 51:400–407, January, 2020. ![Scalable Detection of Concept Drift: A Learning Technique Based on Support Vector Machines [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The issue of concept drift describes how static machine-learning models build on historical data can become unreliable over time and pose a significant challenge to many applications. Although, there is a growing body of literature investigating concept drift existing solutions are often limited to a small number of samples or features and do not work well in Industry 4.0 scenarios. We are proposing a novel algorithm that extends the existing concept drift algorithm FLORA3 by utilizing support vector machines for the classification process. Through this combination of dynamic and static approaches the algorithm is capable of effectively analyzing data streams of high volume. For evaluation, we tested our algorithm on the publicly available data set ‘elec2’, which is based on the energy market in Australia. Our results show that the proposed algorithm needs less computational resources compared to other algorithms while maintaining a high level of accuracy.
@article{altendeitering_scalable_2020,
series = {30th {International} {Conference} on {Flexible} {Automation} and {Intelligent} {Manufacturing} ({FAIM2021})},
title = {Scalable {Detection} of {Concept} {Drift}: {A} {Learning} {Technique} {Based} on {Support} {Vector} {Machines}},
volume = {51},
issn = {2351-9789},
shorttitle = {Scalable {Detection} of {Concept} {Drift}},
url = {http://www.sciencedirect.com/science/article/pii/S2351978920319120},
doi = {10.1016/j.promfg.2020.10.057},
abstract = {The issue of concept drift describes how static machine-learning models build on historical data can become unreliable over time and pose a significant challenge to many applications. Although, there is a growing body of literature investigating concept drift existing solutions are often limited to a small number of samples or features and do not work well in Industry 4.0 scenarios. We are proposing a novel algorithm that extends the existing concept drift algorithm FLORA3 by utilizing support vector machines for the classification process. Through this combination of dynamic and static approaches the algorithm is capable of effectively analyzing data streams of high volume. For evaluation, we tested our algorithm on the publicly available data set ‘elec2’, which is based on the energy market in Australia. Our results show that the proposed algorithm needs less computational resources compared to other algorithms while maintaining a high level of accuracy.},
language = {en},
urldate = {2020-11-23},
journal = {Procedia Manufacturing},
author = {Altendeitering, Marcel and Dübler, Stephan},
month = jan,
year = {2020},
keywords = {FLORA3, SVM, concept drift, energy data, machine learning},
pages = {400--407},
}
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