Academic literature on the topic 'Concept drift'
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Journal articles on the topic "Concept drift"
Museba, Tinofirei, Fulufhelo Nelwamondo, and Khmaies Ouahada. "ADES: A New Ensemble Diversity-Based Approach for Handling Concept Drift." Mobile Information Systems 2021 (June 1, 2021): 1–17. http://dx.doi.org/10.1155/2021/5549300.
Full textZhu, Jiaqi, Shaofeng Cai, Fang Deng, Beng Chin Ooi, and Wenqiao Zhang. "METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection." Proceedings of the VLDB Endowment 17, no. 4 (December 2023): 794–807. http://dx.doi.org/10.14778/3636218.3636233.
Full textSakurai, Guilherme Yukio, Jessica Fernandes Lopes, Bruno Bogaz Zarpelão, and Sylvio Barbon Junior. "Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives." Future Internet 15, no. 5 (April 29, 2023): 169. http://dx.doi.org/10.3390/fi15050169.
Full textToor, Affan Ahmed, Muhammad Usman, Farah Younas, Alvis Cheuk M. Fong, Sajid Ali Khan, and Simon Fong. "Mining Massive E-Health Data Streams for IoMT Enabled Healthcare Systems." Sensors 20, no. 7 (April 9, 2020): 2131. http://dx.doi.org/10.3390/s20072131.
Full textYao, Yuan. "Concept Drift Visualization." Journal of Information and Computational Science 10, no. 10 (July 1, 2013): 3021–29. http://dx.doi.org/10.12733/jics20101915.
Full textWebb, Geoffrey I., Roy Hyde, Hong Cao, Hai Long Nguyen, and Francois Petitjean. "Characterizing concept drift." Data Mining and Knowledge Discovery 30, no. 4 (April 15, 2016): 964–94. http://dx.doi.org/10.1007/s10618-015-0448-4.
Full textYang, Lingkai, Sally McClean, Mark Donnelly, Kevin Burke, and Kashaf Khan. "Detecting and Responding to Concept Drift in Business Processes." Algorithms 15, no. 5 (May 21, 2022): 174. http://dx.doi.org/10.3390/a15050174.
Full textSun, Yange, Zhihai Wang, Yang Bai, Honghua Dai, and Saeid Nahavandi. "A Classifier Graph Based Recurring Concept Detection and Prediction Approach." Computational Intelligence and Neuroscience 2018 (June 7, 2018): 1–13. http://dx.doi.org/10.1155/2018/4276291.
Full textDries, Anton, and Ulrich Rückert. "Adaptive concept drift detection." Statistical Analysis and Data Mining: The ASA Data Science Journal 2, no. 5-6 (November 18, 2009): 311–27. http://dx.doi.org/10.1002/sam.10054.
Full textOrtíz Díaz, Agustín, José del Campo-Ávila, Gonzalo Ramos-Jiménez, Isvani Frías Blanco, Yailé Caballero Mota, Antonio Mustelier Hechavarría, and Rafael Morales-Bueno. "Fast Adapting Ensemble: A New Algorithm for Mining Data Streams with Concept Drift." Scientific World Journal 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/235810.
Full textDissertations / Theses on the topic "Concept drift"
Beyene, Ayne, and Tewelle Welemariam. "Concept Drift in Surgery Prediction." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2330.
Full textHoffmann, Nico, Matthias Kirmse, and Uwe Petersohn. "Approaching Concept Drift by Context Feature Partitioning." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-83954.
Full textGarnett, Roman. "Learning from data streams with concept drift." Thesis, University of Oxford, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.711615.
Full textMarrs, Gary Russell. "Handling latency for online learning with concept drift." Thesis, University of Ulster, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.587478.
Full textAlShammeri, Mohammed. "Dynamic Committees for Handling Concept Drift in Databases (DCCD)." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23498.
Full textMinku, Leandro Lei. "Online ensemble learning in the presence of concept drift." Thesis, University of Birmingham, 2011. http://etheses.bham.ac.uk//id/eprint/1334/.
Full textWidyantoro, Dwi Hendratmo. "Concept drift learning and its application to adaptive information filtering." Diss., Texas A&M University, 2003. http://hdl.handle.net/1969.1/170.
Full textESCOVEDO, TATIANA. "NEUROEVOLUTIVE LEARNING AND CONCEPT DRIFT DETECTION IN NON-STATIONARY ENVIRONMENTS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2015. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=26748@1.
Full textCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Os conceitos do mundo real muitas vezes não são estáveis: eles mudam com o tempo. Assim como os conceitos, a distribuição de dados também pode se alterar. Este problema de mudança de conceitos ou distribuição de dados é conhecido como concept drift e é um desafio para um modelo na tarefa de aprender a partir de dados. Este trabalho apresenta um novo modelo neuroevolutivo com inspiração quântica, baseado em um comitê de redes neurais do tipo Multi-Layer Perceptron (MLP), para a aprendizagem em ambientes não estacionários, denominado NEVE (Neuro-EVolutionary Ensemble). Também apresenta um novo mecanismo de detecção de concept drift, denominado DetectA (Detect Abrupt) com a capacidade de detectar mudanças tanto de forma proativa quanto de forma reativa. O algoritmo evolutivo com inspiração quântica binário-real AEIQ-BR é utilizado no NEVE para gerar automaticamente novos classificadores para o comitê, determinando a topologia mais adequada para a nova rede, selecionando as variáveis de entrada mais apropriadas e determinando todos os pesos da rede neural MLP. O algoritmo AEIQ-R determina os pesos de votação de cada rede neural membro do comitê, sendo possível utilizar votação por combinação linear, votação majoritária ponderada e simples. São implementadas quatro diferentes abordagens do NEVE, que se diferem uma da outra pela forma de detectar e tratar os drifts ocorridos. O trabalho também apresenta resultados de experimentos realizados com o método DetectA e com o modelo NEVE em bases de dados reais e artificiais. Os resultados mostram que o detector se mostrou robusto e eficiente para bases de dados de alta dimensionalidade, blocos de tamanho intermediário, bases de dados com qualquer proporção de drift e com qualquer balanceamento de classes e que, em geral, os melhores resultados obtidos foram usando algum tipo de detecção. Comparando a acurácia do NEVE com outros modelos consolidados da literatura, verifica-se que o NEVE teve acurácia superior na maioria dos casos. Isto reforça que a abordagem por comitê neuroevolutivo é uma escolha robusta para situações em que as bases de dados estão sujeitas a mudanças repentinas de comportamento.
Real world concepts are often not stable: they change with time. Just as the concepts, data distribution may change as well. This problem of change in concepts or distribution of data is known as concept drift and is a challenge for a model in the task of learning from data. This work presents a new neuroevolutive model with quantum inspiration called NEVE (Neuro- EVolutionary Ensemble), based on an ensemble of Multi-Layer Perceptron (MLP) neural networks for learning in non-stationary environments. It also presents a new concept drift detection mechanism, called DetectA (DETECT Abrupt) with the ability to detect changes both proactively as reactively. The evolutionary algorithm with binary-real quantum inspiration AEIQ-BR is used in NEVE to automatically generate new classifiers for the ensemble, determining the most appropriate topology for the new network and by selecting the most appropriate input variables and determining all the weights of the neural network. The AEIQ-R algorithm determines the voting weight of each neural network ensemble member, and you can use voting by linear combination and voting by weighted or simple majority. Four different approaches of NEVE are implemented and they differ from one another by the way of detecting and treating occurring drifts. The work also presents results of experiments conducted with the DetectA method and with the NEVE model in real and artificial databases. The results show that the detector has proved efficient and suitable for data bases with high-dimensionality, intermediate sized blocks, any proportion of drifts and with any class balancing. Comparing the accuracy of NEVE with other consolidated models in the literature, it appears that NEVE had higher accuracy in most cases. This reinforces that the neuroevolution ensemble approach is a robust choice to situations in which the databases are subject to sudden changes in behavior.
Barakat, Lida. "A context-aware approach for handling concept drift in classification." Thesis, Lancaster University, 2018. http://eprints.lancs.ac.uk/124995/.
Full textRAMAMURTHY, SASTHAKUMAR. "TRACKING RECURRENT CONCEPT DRIFT IN STREAMING DATA USING ENSEMBLE CLASSIFIERS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196103577.
Full textBooks on the topic "Concept drift"
Black, Michaela. Learning to classify from temporal data in the presence of concept drift and noise. [S.l: The author], 2002.
Find full textNavajo Nation/National Park Service Team (U.S.) and United States. National Park Service, eds. Antelope Point development concept plan, environmental assessment: Draft. [Window Rock, Ariz.?]: Navajo Nation and National Park Service, 1985.
Find full textUnited States. National Park Service., ed. Environmental assessment: Draft development concept plan : Quinault Area. [Port Angeles, Wash.]: National Park Service, 1988.
Find full textUnited States. National Park Service., ed. Environmental assessment, draft development concept plan: Soleduck area. [Washington, D.C.?]: U.S. Dept. of the Interior, National Park Service, 1988.
Find full textUnited States. National Park Service., ed. Environmental assessment, draft development concept plan: Ozette area. [Washington, D.C.?]: U.S. Dept. of the Interior, National Park Service, 1988.
Find full textUnited States. National Park Service., ed. Environmental assessment, draft development concept plan: Kalaloch area. [Washington, D.C.?]: U.S. Dept. of the Interior, National Park Service, 1988.
Find full textU.S. National Park Service. Ellis Island development concept plan: Draft environmental impact statement. [Washington, D.C.]: U.S. Dept. of the Interior, National Park Service, 2003.
Find full textU.S. National Park Service. Ellis Island development concept plan: Draft environmental impact statement. [Washington, D.C.]: U.S. Dept. of the Interior, National Park Service, 2003.
Find full textU.S. National Park Service. Ellis Island development concept plan: Draft environmental impact statement. [Washington, D.C.]: U.S. Dept. of the Interior, National Park Service, 2003.
Find full textUnited States. National Park Service, ed. Zuma-Trancas Canyons: Draft development concept plan ; environmental assessment. [Washington, D.C.?]: U.S. Dept. of the Interior, National Park Service, 1992.
Find full textBook chapters on the topic "Concept drift"
Shultz, Thomas R., Scott E. Fahlman, Susan Craw, Periklis Andritsos, Panayiotis Tsaparas, Ricardo Silva, Chris Drummond, et al. "Concept Drift." In Encyclopedia of Machine Learning, 202–5. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_153.
Full textSammut, Claude, and Michael Harries. "Concept Drift." In Encyclopedia of Machine Learning and Data Mining, 253–56. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_153.
Full textSayed-Mouchaweh, Moamar. "Handling Concept Drift." In SpringerBriefs in Applied Sciences and Technology, 33–59. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25667-2_3.
Full textMattos, João Guilherme, Thuener Silva, Hélio Lopes, and Alex Laier Bordignon. "Interpretable Concept Drift." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 271–80. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93420-0_26.
Full textGulla, Jon Atle, Geir Solskinnsbakk, Per Myrseth, Veronika Haderlein, and Olga Cerrato. "Concept Signatures and Semantic Drift." In Lecture Notes in Business Information Processing, 101–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22810-0_8.
Full textSeraj, Raihan, and Mohiuddin Ahmed. "Concept Drift for Big Data." In Advanced Sciences and Technologies for Security Applications, 29–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35642-2_2.
Full textGöpfert, Jan Philip, Barbara Hammer, and Heiko Wersing. "Mitigating Concept Drift via Rejection." In Artificial Neural Networks and Machine Learning – ICANN 2018, 456–67. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01418-6_45.
Full textScanlan, Joel, Jacky Hartnett, and Raymond Williams. "DynamicWEB: Adapting to Concept Drift and Object Drift in COBWEB." In AI 2008: Advances in Artificial Intelligence, 454–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89378-3_46.
Full textCase, John, Sanjay Jain, Susanne Kaufmann, Arun Sharma, and Frank Stephan. "Predictive Learning Models for Concept Drift." In Lecture Notes in Computer Science, 276–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-49730-7_21.
Full textBose, R. P. Jagadeesh Chandra, Wil M. P. van der Aalst, Indrė Žliobaitė, and Mykola Pechenizkiy. "Handling Concept Drift in Process Mining." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 391–405. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-642-21640-4_30.
Full textConference papers on the topic "Concept drift"
Pratt, Kevin B., and Gleb Tschapek. "Visualizing concept drift." In the ninth ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/956750.956849.
Full textDries, Anton, and Ulrich Rückert. "Adaptive Concept Drift Detection." In Proceedings of the 2009 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2009. http://dx.doi.org/10.1137/1.9781611972795.21.
Full textYOSHIDA, Kenichi. "Speed of Concept Drift." In 2022 13th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter). IEEE, 2022. http://dx.doi.org/10.1109/iiai-aai-winter58034.2022.00026.
Full textAlmog, Shaked, and Meir Kalech. "Diagnosis for Post Concept Drift Decision Trees Repair." In 20th International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/kr.2023/3.
Full textLiu, Anjin, Yiliao Song, Guangquan Zhang, and Jie Lu. "Regional Concept Drift Detection and Density Synchronized Drift Adaptation." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/317.
Full textBach, Stephen H., and Marcus A. Maloof. "Paired Learners for Concept Drift." In 2008 Eighth IEEE International Conference on Data Mining (ICDM). IEEE, 2008. http://dx.doi.org/10.1109/icdm.2008.119.
Full textHinder, Fabian, André Artelt, Valerie Vaquet, and Barbara Hammer. "Contrasting Explanation of Concept Drift." In ESANN 2022 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2022. http://dx.doi.org/10.14428/esann/2022.es2022-71.
Full textZhao, Lang, Yiqun Zhang, Yuzhu Ji, An Zeng, Fangqing Gu, and Xiaopeng Luo. "Heterogeneous Drift Learning: Classification of Mix-Attribute Data with Concept Drifts." In 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2022. http://dx.doi.org/10.1109/dsaa54385.2022.10032342.
Full textHeng Wang and Zubin Abraham. "Concept drift detection for streaming data." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280398.
Full textXu, Yunwen, Rui Xu, Weizhong Yan, and Paul Ardis. "Concept drift learning with alternating learners." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966109.
Full textReports on the topic "Concept drift"
J.B. Paces, L.A. Neymark, T. Ghezzehei, and P.F. Dobson. Testing the Concept of Drift Shadow at Yucca Mountain, Nevada. Office of Scientific and Technical Information (OSTI), March 2006. http://dx.doi.org/10.2172/893814.
Full textTahmasbi, Ashraf. E-STRSAGA: an ensemble learning method to handle concept drift. Ames (Iowa): Iowa State University, January 2019. http://dx.doi.org/10.31274/cc-20240624-638.
Full textS.J. Altman, A. Forsberg, W. Peplinski, and CK. Ho. Testing the COncept of Drift Shadow with X-Ray Absorption Imaging. Office of Scientific and Technical Information (OSTI), April 2006. http://dx.doi.org/10.2172/894020.
Full textA.A. forsberg, S.J. Altman, W.J. Peplinski, and C.K. Ho. TESTING THE CONCEPT OF DRIFT SHADOW USING X-RAY ABSORPTION IMAGING POSTER. Office of Scientific and Technical Information (OSTI), November 2005. http://dx.doi.org/10.2172/884920.
Full textPeters, Sandra, and Matthew Winters. Comment Letter to the FASB on Measurement in the Financial Statements. CFA Institute, March 2024. http://dx.doi.org/10.56227/24.2.5.
Full textBooker, James L., Richard J. Calantropo, Schuyler R. Porche, and Douglas A. McLiverty. In-Stride Evaluation of Draft Joint Concepts White Paper. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada627206.
Full textTRANSPORTATION SYSTEMS CENTER CAMBRIDGE MA. OSD CALS Architecture Master Plan Study. Data Dictionary. Concept Paper. Draft Version 1.2. Volume 29. Fort Belvoir, VA: Defense Technical Information Center, October 1989. http://dx.doi.org/10.21236/ada265285.
Full textHugo, Jacques, John Forester, David Gertman, Jeffrey Joe, Heather Medema, Julius Persensky, and April Whaley. Draft Function Allocation Framework and Preliminary Technical Basis for Advanced SMR Concepts of Operations. Office of Scientific and Technical Information (OSTI), August 2013. http://dx.doi.org/10.2172/1114571.
Full textJacques Hugo, David Gertman, Jeffrey Joe, Heather Medema, Julius Persensky, and April Whaley. Draft Function Allocation Framework and Preliminary Technical Basis for Advanced SMR Concepts of Operations. Office of Scientific and Technical Information (OSTI), April 2013. http://dx.doi.org/10.2172/1082399.
Full textGreenberg, H., M. Sutton, M. Sharma, and A. Barnwell. REPOSITORY NEAR-FIELD THERMAL MODELING UPDATEINCLUDING ANALYSIS OF OPEN MODE DESIGN CONCEPTS - DRAFT REV. M. Office of Scientific and Technical Information (OSTI), July 2012. http://dx.doi.org/10.2172/1056623.
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