RT Journal Article
T1 LUNAR: 												Cellular automata for drifting data streams
A1 L. Lobo, Jesus
A1 Del Ser, Javier
A1 Herrera, Francisco
AB With the advent of fast data streams, real-time machine learning has become a challenging task, demanding many processing resources. In addition, they can be affected by the concept drift effect, by which learning methods have to detect changes in the data distribution and adapt to these evolving conditions. Several emerging paradigms such as the so-called Smart Dust, Utility Fog, or Swarm Robotics are in need for efficient and scalable solutions in real-time scenarios, and where usually computing resources are constrained. Cellular automata, as low-bias and robust-to-noise pattern recognition methods with competitive classification performance, meet the requirements imposed by the aforementioned paradigms mainly due to their simplicity and parallel nature. In this work we propose LUNAR, a streamified version of cellular automata devised to successfully meet the aforementioned requirements. LUNAR is able to act as a real incremental learner while adapting to drifting conditions. Furthermore, LUNAR is highly interpretable, as its cellular structure represents directly the mapping between the feature space and the labels to be predicted. Extensive simulations with synthetic and real data will provide evidence of its competitive behavior in terms of classification performance when compared to long-established and successful online learning methods.
SN 0020-0255
YR 2021
FD 2021-01-08
LK https://hdl.handle.net/11556/3806
UL https://hdl.handle.net/11556/3806
LA eng
NO L. Lobo , J , Del Ser , J & Herrera , F 2021 , ' LUNAR : Cellular automata for drifting data streams ' , Information Sciences , vol. 543 , pp. 467-487 . https://doi.org/10.1016/j.ins.2020.08.064
NO Publisher Copyright: © 2020 Elsevier Inc.
NO This work has received funding support from the ECSEL Joint Undertaking (JU) under grant agreement No 783163 (iDev40 project). The JU receives support from the European Union’s Horizon 2020 research and innovation programme, national grants from Austria, Belgium, Germany, Italy, Spain and Romania, as well as the European Structural and Investment Funds. It has been also supported by the ELKARTEK program of the Basque Government (Spain) through the VIRTUAL (Ref. KK-2018/00096) research grant. Javier Del Ser has received funding support from the Consolidated Research Group MATHMODE (IT1294-19), granted by the Department of Education of the Basque Government. Francisco Herrera would like to thank the Spanish Government for its funding support (SMART-DaSCI project, TIN2017-89517-P). Finally, we would like to thank John Horton Conway and Tom Fawcett, who recently passed away this year, for their noted contributions to the field of cellular automata and machine learning. This work has received funding support from the ECSEL Joint Undertaking (JU) under grant agreement No 783163 (iDev40 project). The JU receives support from the European Union's Horizon 2020 research and innovation programme, national grants from Austria, Belgium, Germany, Italy, Spain and Romania, as well as the European Structural and Investment Funds. It has been also supported by the ELKARTEK program of the Basque Government (Spain) through the VIRTUAL (Ref. KK-2018/00096) research grant. Javier Del Ser has received funding support from the Consolidated Research Group MATHMODE (IT1294-19), granted by the Department of Education of the Basque Government. Francisco Herrera would like to thank the Spanish Government for its funding support (SMART-DaSCI project, TIN2017-89517-P). Finally, we would like to thank John Horton Conway and Tom Fawcett, who recently passed away this year, for their noted contributions to the field of cellular automata and machine learning.
DS TECNALIA Publications
RD 29 jul 2024