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Dissertations / Theses on the topic 'Concept Drift Detection'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

Ostovar, Alireza. "Business process drift: Detection and characterization." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/127157/1/Alireza_Ostovar_Thesis.pdf.

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This research contributes a set of techniques for the early detection and characterization of process drifts, i.e. statistically significant changes in the behavior of business operations, as recorded in transactional data. Early detection and subsequent characterization of process drifts allows organizations to take prompt remedial actions and avoid potential repercussions resulting from unplanned changes in the behavior of their operations.

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2

ESCOVEDO, 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.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>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 M

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3

Roded, Keren. "The concept of drift and operationalization of its detection in simulated data." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/63135.

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In this paper, the phenomenon of changes in item characteristics over time (often referred to as drift) is discussed from several theoretical perspectives, and a new procedure for the detection of Item Parameter Drift (IPD) is proposed. An initial evaluation of the utility of the proposed procedure is conducted using simulated data modeled by the 2-Parameter Logistic (2PL) Item Response Theory (IRT) model. In addition to the proposed procedure, an IPD analysis of the simulated data is conducted using two known methods: Kim, Cohen, and Park's (1995) extension of Lord's (1980) Chi-square test of

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4

D'Ettorre, Sarah. "Fine-Grained, Unsupervised, Context-based Change Detection and Adaptation for Evolving Categorical Data." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35518.

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Concept drift detection, the identfication of changes in data distributions in streams, is critical to understanding the mechanics of data generating processes and ensuring that data models remain representative through time [2]. Many change detection methods utilize statistical techniques that take numerical data as input. However, many applications produce data streams containing categorical attributes. In this context, numerical statistical methods are unavailable, and different approaches are required. Common solutions use error monitoring, assuming that fluctuations in the error measur

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5

Pesaranghader, Ali. "A Reservoir of Adaptive Algorithms for Online Learning from Evolving Data Streams." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38190.

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Continuous change and development are essential aspects of evolving environments and applications, including, but not limited to, smart cities, military, medicine, nuclear reactors, self-driving cars, aviation, and aerospace. That is, the fundamental characteristics of such environments may evolve, and so cause dangerous consequences, e.g., putting people lives at stake, if no reaction is adopted. Therefore, learning systems need to apply intelligent algorithms to monitor evolvement in their environments and update themselves effectively. Further, we may experience fluctuations regarding the p

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6

Henke, Márcia. "Deteção de Spam baseada na evolução das características com presença de Concept Drift." Universidade Federal do Amazonas, 2015. http://tede.ufam.edu.br/handle/tede/4708.

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Submitted by Geyciane Santos (geyciane_thamires@hotmail.com) on 2015-11-12T20:17:58Z No. of bitstreams: 1 Tese - Márcia Henke.pdf: 2984974 bytes, checksum: a103355c1a7895956d40d4fa9422347a (MD5)<br>Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-11-16T18:36:36Z (GMT) No. of bitstreams: 1 Tese - Márcia Henke.pdf: 2984974 bytes, checksum: a103355c1a7895956d40d4fa9422347a (MD5)<br>Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-11-16T18:43:03Z (GMT) No. of bitstreams: 1 Tese - M

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7

SANTOS, Silas Garrido Teixeira de Carvalho. "Avaliação criteriosa dos algoritmos de detecção de concept drifts." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/17310.

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Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-07-11T12:33:28Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) silas-dissertacao-versao-final-2016.pdf: 1708159 bytes, checksum: 6c0efc5f2f0b27c79306418c9de516f1 (MD5)<br>Made available in DSpace on 2016-07-11T12:33:28Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) silas-dissertacao-versao-final-2016.pdf: 1708159 bytes, checksum: 6c0efc5f2f0b27c79306418c9de516f1 (MD5) Previous issue date: 2015-02-27<br>FACEPE<br>A

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8

Dal, Pozzolo Andrea. "Adaptive Machine Learning for Credit Card Fraud Detection." Doctoral thesis, Universite Libre de Bruxelles, 2015. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/221654.

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Billions of dollars of loss are caused every year by fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to the non-stationary distribution of the data, the highly unbalanced classes distributions and the availability of few transactions labeled by fraud investigators. At the same time public data are scarcely available for confidentiality is

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9

Dong, Yue. "Higher Order Neural Networks and Neural Networks for Stream Learning." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/35731.

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The goal of this thesis is to explore some variations of neural networks. The thesis is mainly split into two parts: a variation of the shaping functions in neural networks and a variation of learning rules in neural networks. In the first part, we mainly investigate polynomial perceptrons - a perceptron with a polynomial shaping function instead of a linear one. We prove the polynomial perceptron convergence theorem and illustrate the notion by showing that a higher order perceptron can learn the XOR function through empirical experiments with implementation. In the second part, we propose

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10

Togbe, Maurras Ulbricht. "Détection distribuée d'anomalies dans les flux de données." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS400.

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La détection d'anomalies est une problématique importante dans de nombreux domaines d'application comme la santé, le transport, l'industrie etc. Il s'agit d'un sujet d'actualité qui tente de répondre à la demande toujours croissante dans différents domaines tels que la détection d'intrusion, de fraude, etc. Dans cette thèse, après un état de l'art général complet, la méthode non supervisé Isolation Forest (IForest) a été étudiée en profondeur en présentant ses limites qui n'ont pas été abordées dans la littérature. Notre nouvelle version de IForest appelée Majority Voting IForest permet d'amél

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11

Zoubeirou, A. Mayaki Mansour. "Méthodes d'apprentissage profond pour la détection d'anomalies et de changement de régimes : application à la maintenance prédictive dans des systèmes embarqués." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4010.

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Dans le contexte de l'Industrie 4.0 et de l'Internet des Objets (IoT), la maintenance prédictive est devenue cruciale pour optimiser la performance et la durée de vie des dispositifs et équipements électroniques. Cette approche, qui repose sur une analyse extensive des données, est basée sur deux concepts essentiels : la détection d'anomalies et la détection de dérive.La détection d'anomalies est essentielle pour identifier les écarts par rapport aux normes établies, signalant des problèmes potentiels tels que les dysfonctionnements des équipements. La détection de dérive, en revanche, suit le

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12

Costa, Fausto Guzzo da. "Employing nonlinear time series analysis tools with stable clustering algorithms for detecting concept drift on data streams." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-13112017-105506/.

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Several industrial, scientific and commercial processes produce open-ended sequences of observations which are referred to as data streams. We can understand the phenomena responsible for such streams by analyzing data in terms of their inherent recurrences and behavior changes. Recurrences support the inference of more stable models, which are deprecated by behavior changes though. External influences are regarded as the main agent actuacting on the underlying phenomena to produce such modifications along time, such as new investments and market polices impacting on stocks, the human interven

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13

Albakour, Subhy. "Stream-automl : automated machine learning overimbalanced data streams for bipartite ranking problems." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT015.

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Malgré sa popularité dans la littérature scientifique, l’apprentissage en ligne doit encore concrétiser son utilité pratique dans les applications industrielles. Vu que l’apprentissage en ligne gère les flux incessants de données volumineuses, à haute vélocité et en évolution constante par conception, le marketing en ligne semble être le candidat favori pour que l’apprentissage en ligne fasse son entrée dans l’industrie. Dans ce contexte, l’état de l’art de l’apprentissage en ligne n’a qu’une utilité limitée, car il se concentre principalement sur les problèmes de classification, tandis que le

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14

Wan, Jones Sai-Wang, and 尹世泓. "Concept Drift Detection Based on Pre-Clustering and Statistical Testing." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/4298j5.

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碩士<br>國立臺灣大學<br>電機工程學研究所<br>105<br>Stream data mining is one of the common data mining methods in real-world applications nowadays. However, it is challenging due to the nature of data stream in real-world, especially concept drift. To handle concept drift, drift detection method is necessary when the accessing data label is unavailable. In this paper, we propose a drift detection method based on the statistical test with clustering as preprocessing and reduce the execution time with principal component analysis (PCA) for the feature extraction method. Experiment result on synthetic and real-w

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15

Chang, Chuan-nan, and 張全男. "Classification of time--changing data streams based on concept drift detection." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/14813010931947505640.

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碩士<br>南華大學<br>資訊管理學研究所<br>96<br>The present paper flows in the discussion material in changes as necessary produces under the concept drifting environment (DataStream) the classification the question. Because this continuously grows under the material environment has One-pass the limit to cause us to be unable to review its histor-icalmaterial. At present already some might the application develop the algorithm. How but do they aim at in retain the material the effectiveness for a period of time to say. But neglects for retain the attempt wrong cost which the effectiveness for a period of time

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16

Chiu, Yao-Ching, and 邱耀慶. "A Parallel Detection and Prediction Method for Concept Drift in Dynamic Data Driven Application System." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/e864zc.

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碩士<br>國立交通大學<br>資訊管理研究所<br>103<br>The traditional data analysis and prediction method assumes that data distribution is stable. Therefore, it can predict unlabeled data precisely by analyzing the historical data. However, in today’s big-data environment, which is changing frequently, the traditional approach can no longer be effective; it cannot handle concept drift in a Dynamic Data Driven Application System (DDDAS). This thesis proposes a parallel detection and prediction method for concept drift in DDDAS. The proposed method can detect changing data and then feedback to the prediction model

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17

Farid, D. M., L. Zhang, A. Hossain, et al. "An adaptive ensemble classifier for mining concept drifting data streams." 2013. http://hdl.handle.net/10454/9573.

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No<br>It is challenging to use traditional data mining techniques to deal with real-time data stream classifications. Existing mining classifiers need to be updated frequently to adapt to the changes in data streams. To address this issue, in this paper we propose an adaptive ensemble approach for classification and novel class detection in concept drifting data streams. The proposed approach uses traditional mining classifiers and updates the ensemble model automatically so that it represents the most recent concepts in data streams. For novel class detection we consider the idea that data po

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18

Renda, Alessandro. "Algorithms and techniques for data stream mining." Doctoral thesis, 2021. http://hdl.handle.net/2158/1235915.

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The abstraction of data streams encompasses a vast range of diverse applications that continuously generate data and therefore require dedicated algorithms and approaches for exploitation and mining. In this framework both unsupervised and supervised approaches are generally employed, depending on the task and on the availability of annotated data. This thesis proposes novel algorithms and techniques specifically tailored for the streaming setting and for knowledge discovery from Social Networks. In the first part of this work we propose a novel clustering algorithm for data streams. Our inv

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19

Obenauff, Alexander. "A progressive learning method for classification of manufacturing errors based on machine data." Master's thesis, 2019. http://hdl.handle.net/10362/76579.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics<br>Manufacturing companies face significant market pressure in today’s globalised world. Fierce global competition and product individualisation mean that production systems require continuous optimisation. This means that automation, flexibility and efficiency have all become vital elements for manufacturers. In this paper, a method based on incremental classification used for manufacturing errors is presented. The analysis and classification focus on data of binary form c

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20

Wu, Tsun-Yuan, and 吳存媛. "Faults and Concept Drifts Detection and Adaptation of Wind Turbines." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/e5vkmq.

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