Skip to main content
Erschienen in: Data Mining and Knowledge Discovery 6/2021

04.09.2021

CURIE: a cellular automaton for concept drift detection

verfasst von: Jesus L. Lobo, Javier Del Ser, Eneko Osaba, Albert Bifet, Francisco Herrera

Erschienen in: Data Mining and Knowledge Discovery | Ausgabe 6/2021

Einloggen

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Data stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as concept drift. Thus, learning models must detect and adapt to such changes, so as to exhibit a good predictive performance after a drift has occurred. In this regard, the development of effective drift detection algorithms becomes a key factor in data stream mining. In this work we propose \(\textit{CURIE}\), a drift detector relying on cellular automata. Specifically, in \(\textit{CURIE}\) the distribution of the data stream is represented in the grid of a cellular automata, whose neighborhood rule can then be utilized to detect possible distribution changes over the stream. Computer simulations are presented and discussed to show that \(\textit{CURIE}\), when hybridized with other base learners, renders a competitive behavior in terms of detection metrics and classification accuracy. \(\textit{CURIE}\) is compared with well-established drift detectors over synthetic datasets with varying drift characteristics.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Anhänge
Nur mit Berechtigung zugänglich
Literatur
Zurück zum Zitat Arrieta AB, Díaz-RodrDíguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, Salvador G, Sergio GL, Daniel M, Richard B et al (2020) Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion 58:82–115CrossRef Arrieta AB, Díaz-RodrDíguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, Salvador G, Sergio GL, Daniel M, Richard B et al (2020) Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion 58:82–115CrossRef
Zurück zum Zitat Bifet A, Holmes G, Pfahringer B, Frank E (2010) Fast perceptron decision tree learning from evolving data streams. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, pp. 299–310 Bifet A, Holmes G, Pfahringer B, Frank E (2010) Fast perceptron decision tree learning from evolving data streams. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, pp. 299–310
Zurück zum Zitat Carvalho Tiago I, Carneiro Murillo G, Oliveira Gina MB (2019) Improving cellular automata scheduling through dynamics control. Int J Parallel Emerg Distrib Syst 34(1):115–141CrossRef Carvalho Tiago I, Carneiro Murillo G, Oliveira Gina MB (2019) Improving cellular automata scheduling through dynamics control. Int J Parallel Emerg Distrib Syst 34(1):115–141CrossRef
Zurück zum Zitat Collados-Lara A-J, Pardo-Igúzquiza E, Pulido-Velazquez D (2019) A distributed cellular automata model to simulate potential future impacts of climate change on snow cover area. Adv Water Resour 124:106–119CrossRef Collados-Lara A-J, Pardo-Igúzquiza E, Pulido-Velazquez D (2019) A distributed cellular automata model to simulate potential future impacts of climate change on snow cover area. Adv Water Resour 124:106–119CrossRef
Zurück zum Zitat Dastjerdi AV, Buyya R (2016) Fog computing: helping the Internet of Things realize its potential. Computer 49(8):112–116CrossRef Dastjerdi AV, Buyya R (2016) Fog computing: helping the Internet of Things realize its potential. Computer 49(8):112–116CrossRef
Zurück zum Zitat Del Ser J, Osaba E, Molina D, Yang XS, Salcedo-Sanz S, Camacho D, Das S, Suganthan PN, Coello CAC, Herrera F (2019) Bio-inspired computation: where we stand and what’s next. Swarm Evolut Comput 48:220–250CrossRef Del Ser J, Osaba E, Molina D, Yang XS, Salcedo-Sanz S, Camacho D, Das S, Suganthan PN, Coello CAC, Herrera F (2019) Bio-inspired computation: where we stand and what’s next. Swarm Evolut Comput 48:220–250CrossRef
Zurück zum Zitat Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30MathSciNetMATH Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30MathSciNetMATH
Zurück zum Zitat Fawcett T (2008) Data mining with cellular automata. ACM SIGKDD Explor Newsl 10(1):32–39CrossRef Fawcett T (2008) Data mining with cellular automata. ACM SIGKDD Explor Newsl 10(1):32–39CrossRef
Zurück zum Zitat Gama J, Žliobaitė I, Bifet A, Pechenizkiy M, Bouchachia A (2014) A survey on concept drift adaptation. ACM Comput Surv (CSUR) 46(4):44CrossRef Gama J, Žliobaitė I, Bifet A, Pechenizkiy M, Bouchachia A (2014) A survey on concept drift adaptation. ACM Comput Surv (CSUR) 46(4):44CrossRef
Zurück zum Zitat Gilpin W (2019) Cellular automata as convolutional neural networks. Phys Rev E 100(3):032402CrossRef Gilpin W (2019) Cellular automata as convolutional neural networks. Phys Rev E 100(3):032402CrossRef
Zurück zum Zitat Gomes HM, Read J, Bifet A, Barddal JP, Gama J (2019) Machine learning for streaming data: state of the art, challenges, and opportunities. ACM SIGKDD Explor Newsl 21(2):6–22CrossRef Gomes HM, Read J, Bifet A, Barddal JP, Gama J (2019) Machine learning for streaming data: state of the art, challenges, and opportunities. ACM SIGKDD Explor Newsl 21(2):6–22CrossRef
Zurück zum Zitat Gonçalves Jr Paulo M, Santos Silas GT, de Carvalho B, Roberto SM, Vieira Davi CL (2014) A comparative study on concept drift detectors. Expert Syst Appl 41(18):8144–8156CrossRef Gonçalves Jr Paulo M, Santos Silas GT, de Carvalho B, Roberto SM, Vieira Davi CL (2014) A comparative study on concept drift detectors. Expert Syst Appl 41(18):8144–8156CrossRef
Zurück zum Zitat Gounaridis D, Chorianopoulos I, Symeonakis E, Koukoulas S (2019) A random forest-cellular automata modelling approach to explore future land use/cover change in attica (Greece), under different socio-economic realities and scales. Sci Total Environ 646:320–335CrossRef Gounaridis D, Chorianopoulos I, Symeonakis E, Koukoulas S (2019) A random forest-cellular automata modelling approach to explore future land use/cover change in attica (Greece), under different socio-economic realities and scales. Sci Total Environ 646:320–335CrossRef
Zurück zum Zitat Hashemi S, Yang Y, Pourkashani M, Kangavari M (2007) To better handle concept change and noise: a cellular automata approach to data stream classification. In: Australasian joint conference on artificial intelligence. Springer, pp. 669–674 Hashemi S, Yang Y, Pourkashani M, Kangavari M (2007) To better handle concept change and noise: a cellular automata approach to data stream classification. In: Australasian joint conference on artificial intelligence. Springer, pp. 669–674
Zurück zum Zitat Hu H, Kantardzic M, Sethi TS (2019) No free lunch theorem for concept drift detection in streaming data classification: a review. In: Wiley interdisciplinary reviews: data mining and knowledge discovery, pp. e1327 Hu H, Kantardzic M, Sethi TS (2019) No free lunch theorem for concept drift detection in streaming data classification: a review. In: Wiley interdisciplinary reviews: data mining and knowledge discovery, pp. e1327
Zurück zum Zitat Ilyas M, Mahgoub I (2018) Smart dust: sensor network applications, architecture and design. CRC Press, Boca RatonCrossRef Ilyas M, Mahgoub I (2018) Smart dust: sensor network applications, architecture and design. CRC Press, Boca RatonCrossRef
Zurück zum Zitat Jie L, Anjin L, Fan D, Feng G, Joao G, Guangquan Z (2018) Learning under concept drift: a review. IEEE Trans Knowl Data Eng 31(12):2346–2363 Jie L, Anjin L, Fan D, Feng G, Joao G, Guangquan Z (2018) Learning under concept drift: a review. IEEE Trans Knowl Data Eng 31(12):2346–2363
Zurück zum Zitat Judy JW (2001) Microelectromechanical systems (mems): fabrication, design and applications. Smart Mater Struct 10(6):1115CrossRef Judy JW (2001) Microelectromechanical systems (mems): fabrication, design and applications. Smart Mater Struct 10(6):1115CrossRef
Zurück zum Zitat Lobo JL, Del Ser J, Laña I, Bilbao MN, Kasabov N (2018) Drift detection over non-stationary data streams using evolving spiking neural networks. In: International symposium on intelligent and distributed computing. Springer, pp. 82–94 Lobo JL, Del Ser J, Laña I, Bilbao MN, Kasabov N (2018) Drift detection over non-stationary data streams using evolving spiking neural networks. In: International symposium on intelligent and distributed computing. Springer, pp. 82–94
Zurück zum Zitat Lobo JL, Del Ser J, Herrera F (2021) LUNAR: Cellular automata for drifting data streams. Inf Sci 543:467–487 Lobo JL, Del Ser J, Herrera F (2021) LUNAR: Cellular automata for drifting data streams. Inf Sci 543:467–487
Zurück zum Zitat Losing V, Hammer B, Wersing H (2018) Incremental on-line learning: a review and comparison of state of the art algorithms. Neurocomputing 1275:1261–1274CrossRef Losing V, Hammer B, Wersing H (2018) Incremental on-line learning: a review and comparison of state of the art algorithms. Neurocomputing 1275:1261–1274CrossRef
Zurück zum Zitat Minku Leandro L, Yao X (2011) DDD: a new ensemble approach for dealing with concept drift. IEEE Trans Knowl Data Eng 24(4):619–633CrossRef Minku Leandro L, Yao X (2011) DDD: a new ensemble approach for dealing with concept drift. IEEE Trans Knowl Data Eng 24(4):619–633CrossRef
Zurück zum Zitat Nemenyi PB (1963) Distribution-free multiple comparisons. Princeton University, Princeton Nemenyi PB (1963) Distribution-free multiple comparisons. Princeton University, Princeton
Zurück zum Zitat Nichele S, Molund A (2017) Deep learning with cellular automaton-based reservoir computing. Complex Systems Nichele S, Molund A (2017) Deep learning with cellular automaton-based reservoir computing. Complex Systems
Zurück zum Zitat Pourkashani M, Kangavari MR (2008) A cellular automata approach to detecting concept drift and dealing with noise. In: 2008 IEEE/ACS international conference on computer systems and applications. IEEE, pp. 142–148 Pourkashani M, Kangavari MR (2008) A cellular automata approach to detecting concept drift and dealing with noise. In: 2008 IEEE/ACS international conference on computer systems and applications. IEEE, pp. 142–148
Zurück zum Zitat Raghavan R (1993) Cellular automata in pattern recognition. Inf Sci 70(1–2):145–177CrossRef Raghavan R (1993) Cellular automata in pattern recognition. Inf Sci 70(1–2):145–177CrossRef
Zurück zum Zitat Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1(5):206–215CrossRef Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1(5):206–215CrossRef
Zurück zum Zitat Ultsch A (2002) Data mining as an application for artificial life. In: Proceedings of the 5th German workshop on artificial life. Citeseer, pp. 191–197 Ultsch A (2002) Data mining as an application for artificial life. In: Proceedings of the 5th German workshop on artificial life. Citeseer, pp. 191–197
Zurück zum Zitat Uzun AO, Usta T, Dündar EB, Korkmaz EE (2018) A solution to the classification problem with cellular automata. Pattern Recog Lett 116:114–120CrossRef Uzun AO, Usta T, Dündar EB, Korkmaz EE (2018) A solution to the classification problem with cellular automata. Pattern Recog Lett 116:114–120CrossRef
Zurück zum Zitat Von Neumann J, Burks AW et al (1966) Theory of self-reproducing automata. IEEE Trans Neural Netw 5(1):3–14 Von Neumann J, Burks AW et al (1966) Theory of self-reproducing automata. IEEE Trans Neural Netw 5(1):3–14
Zurück zum Zitat Webb GI, Hyde R, Cao H, Nguyen HL, Petitjean F (2016) Characterizing concept drift. Data Min Knowl Disc 30(4):964–994MathSciNetCrossRef Webb GI, Hyde R, Cao H, Nguyen HL, Petitjean F (2016) Characterizing concept drift. Data Min Knowl Disc 30(4):964–994MathSciNetCrossRef
Zurück zum Zitat Wolfram S (2002) A new kind of science. Wolfram media Champaign, ChampaignMATH Wolfram S (2002) A new kind of science. Wolfram media Champaign, ChampaignMATH
Zurück zum Zitat Žliobaitè I, Pechenizkiy M, Gama J (2016) An overview of concept drift applications. In: Big data analysis: new algorithms for a new society. Springer, pp. 91–114 Žliobaitè I, Pechenizkiy M, Gama J (2016) An overview of concept drift applications. In: Big data analysis: new algorithms for a new society. Springer, pp. 91–114
Metadaten
Titel
CURIE: a cellular automaton for concept drift detection
verfasst von
Jesus L. Lobo
Javier Del Ser
Eneko Osaba
Albert Bifet
Francisco Herrera
Publikationsdatum
04.09.2021
Verlag
Springer US
Erschienen in
Data Mining and Knowledge Discovery / Ausgabe 6/2021
Print ISSN: 1384-5810
Elektronische ISSN: 1573-756X
DOI
https://doi.org/10.1007/s10618-021-00776-2

Weitere Artikel der Ausgabe 6/2021

Data Mining and Knowledge Discovery 6/2021 Zur Ausgabe