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Dissertations / Theses on the topic 'Concept drift'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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.

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Context: In healthcare, the decision of patient referral evolves through time because of changes in scientific developments, and clinical practices. Existing decision support systems of patient referral are based on the expert systems approach. This usually requires manual updates when changes in clinical practices occur. Automatically updating the decision support system by identifying and handling so-called concept drift improves the efficiency of healthcare systems. In the stateof-the- art, there are only specific ways of handling concept drift; developing a more generic technique which wor

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2

Hoffmann, 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.

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In this paper we present a new approach to handle concept drift using domain-specific knowledge. More precisely, we capitalize known context features to partition a domain into subdomains featuring static class distributions. Subsequently, we learn separate classifiers for each sub domain and classify new instances accordingly. To determine the optimal partitioning for a domain we apply a search algorithm aiming to maximize the resulting accuracy. In practical domains like fault detection concept drift often occurs in combination with imbalances data. As this issue gets more important learnin

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3

Garnett, Roman. "Learning from data streams with concept drift." Thesis, University of Oxford, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.711615.

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Marrs, 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.

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We live in a world of ever-increasing amounts of data. There is a need to devise better and increasingly automated systems for analyzing and utilising such data, from online data streams, for the purposes of classification and prediction. Across many domains such as banking, financial markets, network management and even in biomedical monitoring of pathogen sensitivity to drugs, the competitive edge is gained by those who act on their data fastest, most accurately and keep up to date with any changes occurring in their domain. This has led to the rise of research into online learners. These au

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AlShammeri, Mohammed. "Dynamic Committees for Handling Concept Drift in Databases (DCCD)." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23498.

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Concept drift refers to a problem that is caused by a change in the data distribution in data mining. This leads to reduction in the accuracy of the current model that is used to examine the underlying data distribution of the concept to be discovered. A number of techniques have been introduced to address this issue, in a supervised learning (or classification) setting. In a classification setting, the target concept (or class) to be learned is known. One of these techniques is called “Ensemble learning”, which refers to using multiple trained classifiers in order to get better predictions by

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Minku, Leandro Lei. "Online ensemble learning in the presence of concept drift." Thesis, University of Birmingham, 2011. http://etheses.bham.ac.uk//id/eprint/1334/.

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In online learning, each training example is processed separately and then discarded. Environments that require online learning are often non-stationary and their underlying distributions may change over time (concept drift). Even though ensembles of learning machines have been used for handling concept drift, there has been no deep study of why they can be helpful for dealing with drifts and which of their features can contribute for that. The thesis mainly investigates how ensemble diversity affects accuracy in online learning in the presence of concept drift and how to use diversity in orde

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Widyantoro, 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.

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Tracking the evolution of user interests is a problem instance of concept drift learning. Keeping track of multiple interest categories is a natural phenomenon as well as an interesting tracking problem because interests can emerge and diminish at different time frames. The first part of this dissertation presents a Multiple Three-Descriptor Representation (MTDR) algorithm, a novel algorithm for learning concept drift especially built for tracking the dynamics of multiple target concepts in the information filtering domain. The learning process of the algorithm combines the long-term and short

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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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9

Barakat, Lida. "A context-aware approach for handling concept drift in classification." Thesis, Lancaster University, 2018. http://eprints.lancs.ac.uk/124995/.

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Adapting classification models to changes is one of the main challenges associated with learning from data in dynamic environments. In particular, the description of the target concept is not static and may change over time under the influence of varying environmental conditions (i.e. varying context). Although many adaptive learning approaches have been proposed in the literature to address such changes, these are limited in terms of the extent to which the contextual aspects are explicitly identified and utilised. Instead, existing approaches mostly rely on monitoring the effects of drift (i

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10

RAMAMURTHY, 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.

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11

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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Almeida, Paulo Ricardo Lisboa de. "Adapting the dynamic selection of classifiers approach for concept drift scenarios." reponame:Repositório Institucional da UFPR, 2017. http://hdl.handle.net/1884/52771.

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Orientador : Luiz Eduardo S. de Oliveira<br>Coorientadores : Alceu de Souza Britto Jr. ; Robert Sabourin<br>Tese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 09/11/2017<br>Inclui referências : f. 143-154<br>Resumo: Muitos ambientes podem sofrer com mudanças nas distribuições ou nas probabilidades a posteriori com o decorrer do tempo, em um problema conhecido como Concept Drift. Nesses cenários, é imperativa a implementação de algum mecanismo para adaptar o sistema de classificação às mudanças no ambiente a f

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Alzogbi, Anas [Verfasser], and Georg [Akademischer Betreuer] Lausen. "Recommending scientific publications: addressing the one-class problem and concept drift." Freiburg : Universität, 2019. http://d-nb.info/1185391312/34.

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Pinagé, Felipe Azevedo, and 92-98187-1016. "Handling Concept Drift Based on Data Similarity and Dynamic Classifier Selection." Universidade Federal do Amazonas, 2017. http://tede.ufam.edu.br/handle/tede/5956.

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Conca, Piero. "An adaptive framework for classification of concept drift with limited supervision." Thesis, University of York, 2012. http://etheses.whiterose.ac.uk/5587/.

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This thesis deals with the problem of classification of data affected by concept drift. In particular, it investigates the area of unsupervised model updating in which a classification model is updated without using information about the changing distributions of the classes. An adaptive framework that contains an ensemble of classifiers is developed. These can be mature or naive. In particular, only mature classifiers generate decisions, through majority voting, while naive classifiers are candidate to become mature. The first novelty of the proposed framework is a technique of feedback that

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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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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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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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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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Black, Michaela. "Learning to classify from temporal data in the presence of concept drift and noise." Thesis, University of Ulster, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.232851.

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CAVALCANTE, Rodolfo Carneiro. "An adaptive learning system for time series forecasting in the presence of concept drift." Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/25349.

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Submitted by Pedro Barros (pedro.silvabarros@ufpe.br) on 2018-08-01T20:38:56Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) TESE Rodolfo Carneiro Cavalcante.pdf: 4472525 bytes, checksum: b8913f87ac611abb2701ce3e4918cbcb (MD5)<br>Approved for entry into archive by Alice Araujo (alice.caraujo@ufpe.br) on 2018-08-02T20:05:14Z (GMT) No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) TESE Rodolfo Carneiro Cavalcante.pdf: 4472525 bytes, checksum: b8913f87ac611abb2701ce3e4918cbcb (MD5)<br>Made available in DSpa

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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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Schnackenberg, Sarah Anna [Verfasser], Uwe [Akademischer Betreuer] Ligges, and Claus [Gutachter] Weihs. "Online Diskriminanzanalyse für Datensituationen mit Concept Drift / Sarah Anna Schnackenberg ; Gutachter: Claus Weihs ; Betreuer: Uwe Ligges." Dortmund : Universitätsbibliothek Dortmund, 2020. http://d-nb.info/1228214336/34.

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Bridle, Robert Angus, and robert bridle@gmail com. "Adaptive User Interfaces for Mobile Computing Devices." The Australian National University. College of Engineering and Computer Sciences, 2008. http://thesis.anu.edu.au./public/adt-ANU20081117.184430.

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This thesis examines the use of adaptive user interface elements on a mobile phone and presents two adaptive user interface approaches. The approaches attempt to increase the efficiency with which a user interacts with a mobile phone, while ensuring the interface remains predictable to a user. ¶ An adaptive user interface approach is presented that predicts the menu item a user will select. When a menu is opened, the predicted menu item is highlighted instead of the top-most menu item. The aim is to maintain the layout of the menu and to save the user from performing scrolling key presses. A m

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Joe-Yen, Stefan. "Performance Envelopes of Adaptive Ensemble Data Stream Classifiers." NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1014.

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This dissertation documents a study of the performance characteristics of algorithms designed to mitigate the effects of concept drift on online machine learning. Several supervised binary classifiers were evaluated on their performance when applied to an input data stream with a non-stationary class distribution. The selected classifiers included ensembles that combine the contributions of their member algorithms to improve overall performance. These ensembles adapt to changing class definitions, known as “concept drift,” often present in real-world situations, by adjusting the relative contr

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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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Baier, Lucas [Verfasser], and G. [Akademischer Betreuer] Satzger. "Concept Drift Handling in Information Systems: Preserving the Validity of Deployed Machine Learning Models / Lucas Baier ; Betreuer: G. Satzger." Karlsruhe : KIT-Bibliothek, 2021. http://d-nb.info/1241189250/34.

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Rakitianskaia, A. S. (Anastassia Sergeevna). "Using particle swarm optimisation to train feedforward neural networks in dynamic environments." Diss., University of Pretoria, 2011. http://hdl.handle.net/2263/28618.

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The feedforward neural network (NN) is a mathematical model capable of representing any non-linear relationship between input and output data. It has been succesfully applied to a wide variety of classification and function approximation problems. Various neural network training algorithms were developed, including the particle swarm optimiser (PSO), which was shown to outperform the standard back propagation training algorithm on a selection of problems. However, it was usually assumed that the environment in which a NN operates is static. Such an assumption is often not valid for real life p

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Belcin, Andrei. "Smart Cube Predictions for Online Analytic Query Processing in Data Warehouses." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/41956.

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A data warehouse (DW) is a transformation of many sources of transactional data integrated into a single collection that is non-volatile and time-variant that can provide decision support to managerial roles within an organization. For this application, the database server needs to process multiple users’ queries by joining various datasets and loading the result in main memory to begin calculations. In current systems, this process is reactionary to users’ input and can be undesirably slow. In previous studies, it was shown that a personalization scheme of a single user’s query patterns an

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Floyd, Sean Louis Alan. "Semi-Supervised Hybrid Windowing Ensembles for Learning from Evolving Streams." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39273.

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In this thesis, learning refers to the intelligent computational extraction of knowledge from data. Supervised learning tasks require data to be annotated with labels, whereas for unsupervised learning, data is not labelled. Semi-supervised learning deals with data sets that are partially labelled. A major issue with supervised and semi-supervised learning of data streams is late-arriving or missing class labels. Assuming that correctly labelled data will always be available and timely is often unfeasible, and, as such, supervised methods are not directly applicable in the real world. Therefor

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Jaber, Ghazal. "An approach for online learning in the presence of concept changes." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00907486.

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Learning from data streams is emerging as an important application area. When the environment changes, it is necessary to rely on on-line learning with the capability to adapt to changing conditions a.k.a. concept drifts. Adapting to concept drifts entails forgetting some or all of the old acquired knowledge when the concept changes while accumulating knowledge regarding the supposedly stationary underlying concept. This tradeoff is called the stability-plasticity dilemma. Ensemble methods have been among the most successful approaches. However, the management of the ensemble which ultimately

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Mohammad, Rami Mustafa A. "An ensemble self-structuring neural network approach to solving classification problems with virtual concept drift and its application to phishing websites." Thesis, University of Huddersfield, 2016. http://eprints.hud.ac.uk/id/eprint/30188/.

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Classification in data mining is one of the well-known tasks that aim to construct a classification model from a labelled input data set. Most classification models are devoted to a static environment where the complete training data set is presented to the classification algorithm. This data set is assumed to cover all information needed to learn the pertinent concepts (rules and patterns) related to how to classify unseen examples to predefined classes. However, in dynamic (non-stationary) domains, the set of features (input data attributes) may change over time. For instance, some features

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Malik, Muhammad Hamza. "Information extraction and mapping for KG construction with learned concepts from scientic documents : Experimentation with relations data for development of concept learner." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285572.

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Systematic review of research manuscripts is a common procedure in which research studies pertaining a particular field or domain are classified and structured in a methodological way. This process involves, between other steps, an extensive review and consolidation of scientific metrics and attributes of the manuscripts, such as citations, type or venue of publication. The extraction and mapping of relevant publication data, evidently, is a very laborious task if performed manually. Automation of such systematic mapping steps intend to reduce the human effort required and therefore can potent

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Diaz, Jorge Cristhian Chamby. "An incremental gaussian mixture network for data stream classification in non-stationary environments." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/174484.

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Classificação de fluxos contínuos de dados possui muitos desafios para a comunidade de mineração de dados quando o ambiente não é estacionário. Um dos maiores desafios para a aprendizagem em fluxos contínuos de dados está relacionado com a adaptação às mudanças de conceito, as quais ocorrem como resultado da evolução dos dados ao longo do tempo. Duas formas principais de desenvolver abordagens adaptativas são os métodos baseados em conjunto de classificadores e os algoritmos incrementais. Métodos baseados em conjunto de classificadores desempenham um papel importante devido à sua modularidade,

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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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Olorunnimbe, Muhammed. "Intelligent Adaptation of Ensemble Size in Data Streams Using Online Bagging." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32340.

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In this era of the Internet of Things and Big Data, a proliferation of connected devices continuously produce massive amounts of fast evolving streaming data. There is a need to study the relationships in such streams for analytic applications, such as network intrusion detection, fraud detection and financial forecasting, amongst other. In this setting, it is crucial to create data mining algorithms that are able to seamlessly adapt to temporal changes in data characteristics that occur in data streams. These changes are called concept drifts. The resultant models produced by such algorithms

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Oliveira, Luan Soares. "Classificação de fluxos de dados não estacionários com algoritmos incrementais baseados no modelo de misturas gaussianas." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-06042016-143503/.

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Aprender conceitos provenientes de fluxos de dados é uma tarefa significamente diferente do aprendizado tradicional em lote. No aprendizado em lote, existe uma premissa implicita que os conceitos a serem aprendidos são estáticos e não evoluem significamente com o tempo. Por outro lado, em fluxos de dados os conceitos a serem aprendidos podem evoluir ao longo do tempo. Esta evolução é chamada de mudança de conceito, e torna a criação de um conjunto fixo de treinamento inaplicável neste cenário. O aprendizado incremental é uma abordagem promissora para trabalhar com fluxos de dados. Contudo,

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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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Žliobaitė, Indrė. "Adaptive Training Set Formation." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20100416_094953-42662.

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Nowadays, when the environment is changing rapidly and dynamically, there is a particular need for adaptive data mining methods. `Spam' filters, personalized recommender and marketing systems, network intrusion detection systems, business prediction and decision support systems need to be regularly retrained to take into account changing nature of the data. In the stationary settings the more data is at hand, the more accurate model can be trained. In the changing environment an old data decreases the accuracy. In such a case only a subset of the historical data might be selected to form a tra

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Žliobaitė, Indrė. "Adaptyvus mokymo imties formavimas." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20100416_095003-09795.

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Šiandieninėje, dinamiškai besikeičiančioje aplinkoje reikalingi adaptyvūs duomenų gavybos metodai. Nepageidaujamų laiškų klasifikatoriai, asmeninio rekomendavimo ir rinkodaros, įsilaužimų į kompiuterinius tinklus aptikimo, verslo rodiklių prognozavimo bei sprendimų priėmimo sistemos turi nuolat “persimokyti”, reaguoti į besikeičiančius duomenis. Stacionarioje aplinkoje kuo daugiau mokymo duomenų - tuo tikslesnis modelis. Besikeičiančioje aplinkoje seni duomenys blogina tikslumą. Tokiu atveju, vietoje visų turimų istorinių duomenų panaudojimo, gali būti tikslingai išrenkama tik tam tikra jų dal

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Reis, Denis Moreira dos. "Classificação de fluxos de dados com mudança de conceito e latência de verificação." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-13012017-095800/.

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Apesar do grau relativamente alto de maturidade existente na área de pesquisa de aprendizado supervisionado em lote, na qual são utilizados dados originários de problemas estacionários, muitas aplicações reais lidam com fluxos de dados cujas distribuições de probabilidade se alteram com o tempo, ocasionando mudanças de conceito. Diversas pesquisas vêm sendo realizadas nos últimos anos com o objetivo de criar modelos precisos mesmo na presença de mudanças de conceito. A maioria delas, no entanto, assume que tão logo um evento seja classificado pelo algoritmo de aprendizado, seu rótulo verdadeir

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Montiel, López Jacob. "Fast and slow machine learning." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT014/document.

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L'ère du Big Data a révolutionné la manière dont les données sont créées et traitées. Dans ce contexte, de nombreux défis se posent, compte tenu de la quantité énorme de données disponibles qui doivent être efficacement gérées et traitées afin d’extraire des connaissances. Cette thèse explore la symbiose de l'apprentissage en mode batch et en flux, traditionnellement considérés dans la littérature comme antagonistes, sur le problème de la classification à partir de flux de données en évolution. L'apprentissage en mode batch est une approche bien établie basée sur une séquence finie: d'abord le

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Montiel, López Jacob. "Fast and slow machine learning." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT014.

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L'ère du Big Data a révolutionné la manière dont les données sont créées et traitées. Dans ce contexte, de nombreux défis se posent, compte tenu de la quantité énorme de données disponibles qui doivent être efficacement gérées et traitées afin d’extraire des connaissances. Cette thèse explore la symbiose de l'apprentissage en mode batch et en flux, traditionnellement considérés dans la littérature comme antagonistes, sur le problème de la classification à partir de flux de données en évolution. L'apprentissage en mode batch est une approche bien établie basée sur une séquence finie: d'abord le

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Loeffel, Pierre-Xavier. "Algorithmes de machine learning adaptatifs pour flux de données sujets à des changements de concept." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066496/document.

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Dans cette thèse, nous considérons le problème de la classification supervisée sur un flux de données sujets à des changements de concepts. Afin de pouvoir apprendre dans cet environnement, nous pensons qu’un algorithme d’apprentissage doit combiner plusieurs caractéristiques. Il doit apprendre en ligne, ne pas faire d’hypothèses sur le concept ou sur la nature des changements de concepts et doit être autorisé à s’abstenir de prédire lorsque c’est nécessaire. Les algorithmes en ligne sont un choix évident pour traiter les flux de données. De par leur structure, ils sont capables de continuelle

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Albuquerque, Regis Antonio Saraiva, and 68999536833. "Seleção dinâmica de comitês de classificadores baseada em diversidade e acurácia para detecção de mudança de conceitos." Universidade Federal do Amazonas, 2018. https://tede.ufam.edu.br/handle/tede/6480.

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Submitted by Regis Albuquerque (regis.albuquerque1@gmail.com) on 2018-06-20T21:40:28Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) dissertacao_regis_corrigida_final.pdf: 2557634 bytes, checksum: b48eb7c37fd9dd633c4489a7f0f041a4 (MD5)<br>Approved for entry into archive by Secretaria PPGI (secretariappgi@icomp.ufam.edu.br) on 2018-06-20T21:52:37Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) dissertacao_regis_corrigida_final.pdf: 2557634 bytes, checksum: b48eb7c37fd9dd633c4489a7f0f041a4 (MD5)<

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Loeffel, Pierre-Xavier. "Algorithmes de machine learning adaptatifs pour flux de données sujets à des changements de concept." Electronic Thesis or Diss., Paris 6, 2017. http://www.theses.fr/2017PA066496.

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Dans cette thèse, nous considérons le problème de la classification supervisée sur un flux de données sujets à des changements de concepts. Afin de pouvoir apprendre dans cet environnement, nous pensons qu’un algorithme d’apprentissage doit combiner plusieurs caractéristiques. Il doit apprendre en ligne, ne pas faire d’hypothèses sur le concept ou sur la nature des changements de concepts et doit être autorisé à s’abstenir de prédire lorsque c’est nécessaire. Les algorithmes en ligne sont un choix évident pour traiter les flux de données. De par leur structure, ils sont capables de continuelle

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Nunes, André Luís. "Um estudo investigativo de algoritmos de regressão para data streams." Universidade do Vale do Rio dos Sinos, 2017. http://www.repositorio.jesuita.org.br/handle/UNISINOS/6345.

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Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2017-06-13T14:22:04Z No. of bitstreams: 1 André Luís Nunes_.pdf: 2523682 bytes, checksum: 5e3899cfac6d76db6b2c6ac16b7f5325 (MD5)<br>Made available in DSpace on 2017-06-13T14:22:04Z (GMT). No. of bitstreams: 1 André Luís Nunes_.pdf: 2523682 bytes, checksum: 5e3899cfac6d76db6b2c6ac16b7f5325 (MD5) Previous issue date: 2017-03-28<br>Nenhuma<br>A explosão no volume de dados e a sua velocidade de expansão tornam as tarefas de descoberta do conhecimento e a análise de dados desafiantes, ainda mais quando consideradas bases não-estacionárias

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Ellis, Mathys. "Regularised feed forward neural networks for streamed data classification problems." Diss., University of Pretoria, 2020. http://hdl.handle.net/2263/75804.

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Streamed data classiﬁcation problems (SDCPs) require classifiers with the ability to learn and to adjust to the underlying relationships in data streams, in real-time. This requirement poses a challenge to classifiers, because the learning task is no longer just to ﬁnd the optimal decision boundaries, but also to track changes in the decision boundaries as new training data is received. The challenge is due to concept drift, i.e. the changing of decision boundaries over time. Changes include disappearing, appearing, or shifting decision boundaries. This thesis proposes an online learning appro

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Jarosch, Martin. "Klasifikace v proudu dat pomocí souboru klasifikátorů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-235468.

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This master's thesis deals with knowledge discovery and is focused on data stream classification. Three ensemble classification methods are described here. These methods are implemented in practical part of this thesis and are included in the classification system. Extensive measurements and experimentation were used for method analysis and comparison. Implemented methods were then integrated into Malware analysis system. At the conclusion are presented obtained results.

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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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