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Academic literature on the topic 'Concept drift'

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Published: 4 June 2021
Last updated: 20 July 2024

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Journal articles on the topic "Concept drift"

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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.

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Beyond applying machine learning predictive models to static tasks, a significant corpus of research exists that applies machine learning predictive models to streaming environments that incur concept drift. With the prevalence of streaming real-world applications that are associated with changes in the underlying data distribution, the need for applications that are capable of adapting to evolving and time-varying dynamic environments can be hardly overstated. Dynamic environments are nonstationary and change with time and the target variables to be predicted by the learning algorithm and often evolve with time, a phenomenon known as concept drift. Most work in handling concept drift focuses on updating the prediction model so that it can recover from concept drift while little effort has been dedicated to the formulation of a learning system that is capable of learning different types of drifting concepts at any time with minimum overheads. This work proposes a novel and evolving data stream classifier called Adaptive Diversified Ensemble Selection Classifier (ADES) that significantly optimizes adaptation to different types of concept drifts at any time and improves convergence to new concepts by exploiting different amounts of ensemble diversity. The ADES algorithm generates diverse base classifiers, thereby optimizing the margin distribution to exploit ensemble diversity to formulate an ensemble classifier that generalizes well to unseen instances and provides fast recovery from different types of concept drift. Empirical experiments conducted on both artificial and real-world data streams demonstrate that ADES can adapt to different types of drifts at any given time. The prediction performance of ADES is compared to three other ensemble classifiers designed to handle concept drift using both artificial and real-world data streams. The comparative evaluation performed demonstrated the ability of ADES to handle different types of concept drifts. The experimental results, including statistical test results, indicate comparable performances with other algorithms designed to handle concept drift and prove their significance and effectiveness.
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Zhu, 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.

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Real-time analytics and decision-making require online anomaly detection (OAD) to handle drifts in data streams efficiently and effectively. Unfortunately, existing approaches are often constrained by their limited detection capacity and slow adaptation to evolving data streams, inhibiting their efficacy and efficiency in handling concept drift , which is a major challenge in evolving data streams. In this paper, we introduce METER, a novel dynamic concept adaptation framework that introduces a new paradigm for OAD. METER addresses concept drift by first training a base detection model on historical data to capture recurring central concepts , and then learning to dynamically adapt to new concepts in data streams upon detecting concept drift. Particularly, METER employs a novel dynamic concept adaptation technique that leverages a hypernetwork to dynamically generate the parameter shift of the base detection model, providing a more effective and efficient solution than conventional retraining or fine-tuning approaches. Further, METER incorporates a lightweight drift detection controller, underpinned by evidential deep learning, to support robust and interpretable concept drift detection. We conduct an extensive experimental evaluation, and the results show that METER significantly outperforms existing OAD approaches in various application scenarios.
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Sakurai, 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.

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The stream mining paradigm has become increasingly popular due to the vast number of algorithms and methodologies it provides to address the current challenges of Internet of Things (IoT) and modern machine learning systems. Change detection algorithms, which focus on identifying drifts in the data distribution during the operation of a machine learning solution, are a crucial aspect of this paradigm. However, selecting the best change detection method for different types of concept drift can be challenging. This work aimed to provide a benchmark for four drift detection algorithms (EDDM, DDM, HDDMW, and HDDMA) for abrupt, gradual, and incremental drift types. To shed light on the capacity and possible trade-offs involved in selecting a concept drift algorithm, we compare their detection capability, detection time, and detection delay. The experiments were carried out using synthetic datasets, where various attributes, such as stream size, the amount of drifts, and drift duration can be controlled and manipulated on our generator of synthetic stream. Our results show that HDDMW provides the best trade-off among all performance indicators, demonstrating superior consistency in detecting abrupt drifts, but has suboptimal time consumption and a limited ability to detect incremental drifts. However, it outperforms other algorithms in detection delay for both abrupt and gradual drifts with an efficient detection performance and detection time performance.
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Toor, 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.

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With the increasing popularity of the Internet-of-Medical-Things (IoMT) and smart devices, huge volumes of data streams have been generated. This study aims to address the concept drift, which is a major challenge in the processing of voluminous data streams. Concept drift refers to overtime change in data distribution. It may occur in the medical domain, for example the medical sensors measuring for general healthcare or rehabilitation, which may switch their roles for ICU emergency operations when required. Detecting concept drifts becomes trickier when the class distributions in data are skewed, which is often true for medical sensors e-health data. Reactive Drift Detection Method (RDDM) is an efficient method for detecting long concepts. However, RDDM has a high error rate, and it does not handle class imbalance. We propose an Enhanced Reactive Drift Detection Method (ERDDM), which systematically generates strategies to handle concept drift with class imbalance in data streams. We conducted experiments to compare ERDDM with three contemporary techniques in terms of prediction error, drift detection delay, latency, and ability to handle data imbalance. The experimentation was done in Massive Online Analysis (MOA) on 48 synthetic datasets customized to possess the capabilities of data streams. ERDDM can handle abrupt and gradual drifts and performs better than all benchmarks in almost all experiments.
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Yao, 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.

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Webb, 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.

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Yang, 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.

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Concept drift, which refers to changes in the underlying process structure or customer behaviour over time, is inevitable in business processes, causing challenges in ensuring that the learned model is a proper representation of the new data. Due to factors such as seasonal effects and policy updates, concept drifts can occur in customer transitions and time spent throughout the process, either suddenly or gradually. In a concept drift context, we can discard the old data and retrain the model using new observations (sudden drift) or combine the old data with the new data to update the model (gradual drift) or maintain the model as unchanged (no drift). In this paper, we model a response to concept drift as a sequential decision making problem by combing a hierarchical Markov model and a Markov decision process (MDP). The approach can detect concept drift, retrain the model and update customer profiles automatically. We validate the proposed approach on 68 artificial datasets and a real-world hospital billing dataset, with experimental results showing promising performance.
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Sun, 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.

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It is common in real-world data streams that previously seen concepts will reappear, which suggests a unique kind of concept drift, known as recurring concepts. Unfortunately, most of existing algorithms do not take full account of this case. Motivated by this challenge, a novel paradigm was proposed for capturing and exploiting recurring concepts in data streams. It not only incorporates a distribution-based change detector for handling concept drift but also captures recurring concept by storing recurring concepts in a classifier graph. The possibility of detecting recurring drifts allows reusing previously learnt models and enhancing the overall learning performance. Extensive experiments on both synthetic and real-world data streams reveal that the approach performs significantly better than the state-of-the-art algorithms, especially when concepts reappear.
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Dries, 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.

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Ortíz 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.

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The treatment of large data streams in the presence of concept drifts is one of the main challenges in the field of data mining, particularly when the algorithms have to deal with concepts that disappear and then reappear. This paper presents a new algorithm, called Fast Adapting Ensemble (FAE), which adapts very quickly to both abrupt and gradual concept drifts, and has been specifically designed to deal with recurring concepts. FAE processes the learning examples in blocks of the same size, but it does not have to wait for the batch to be complete in order to adapt its base classification mechanism. FAE incorporates a drift detector to improve the handling of abrupt concept drifts and stores a set of inactive classifiers that represent old concepts, which are activated very quickly when these concepts reappear. We compare our new algorithm with various well-known learning algorithms, taking into account, common benchmark datasets. The experiments show promising results from the proposed algorithm (regarding accuracy and runtime), handling different types of concept drifts.
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Dissertations / Theses on the topic "Concept drift"

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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 works regardless of restrictions on how slow, fast, sudden, gradual, local, global, cyclical, noisy or otherwise changes in internal distribution, is still a challenge. Objectives: An algorithm that handles concept drift in surgery prediction is investigated. Concept drift detection techniques are evaluated to find out a suitable detection technique in the context of surgery prediction. Moreover, a plausible combination of detection and handling algorithms including the proposed algorithm, Trigger Based Ensemble (TBE)+, are evaluated on hospital data. Method: Experiments are conducted to investigates the impact of concept drift on prediction performance and to reduce concept drift impact. The experiments compare three existing methods (AWE, Active Classifier, Learn++) and the proposed algorithm, Trigger Based Ensemble(TBE). Real-world dataset from orthopedics department of Belkinge hospital and other domain dataset are used in the experiment. Results: The negative impact of concept drift in surgery prediction is investigated. The relationship between temporal changes in data distribution and surgery prediction concept drift is identified. Furthermore, the proposed algorithm is evaluated and compared with existing handling approaches. Conclusion: The proposed algorithm, Trigger Based Ensemble (TBE), is capable of detecting the occurrences of concept drifts and to adapt quickly to various changes. The Trigger Based Ensemble algorithm performed comparatively better or sometimes similar to the existing concept drift handling algorithms in the absence of noise. Moreover, the performance of Trigger Based Ensemble is consistent for small and large dataset. The research is of twofold contributions, in that it is improving surgery prediction performance as well as contributing one competitive concept drift handling algorithm to the area of computer science.
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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 learning models on smaller subdomains we additionally use sampling methods to handle it. Comparative experiments with artificial data sets showed that our approach outperforms a plain SVM regarding different performance measures. Summarized, the partitioning concept drift approach (PCD) is a possible way to handle concept drift in domains where the causing context features are at least partly known.
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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 automated systems serve to train themselves on received data and discover rules for use in classification and prediction. They serve to keep those rules up to date as concept drift, i.e. changing of the underlying rules, occurs. However, to date, there has been little undertaken into research as to how latency in the data stream impacts upon such learning. This thesis examines the hypothesis that latency can have a substantial impact upon the performance of online learners operating on domains with concept drift, and, that key meta-data attributes describing example passage throughout the domain may help to resolve such issues. The thesis explores what it means to be a domain by developing a generic model. The assumptions that are applied in current research upon the nature of example arrival are considered and challenged. A framework, ELISE, for simulating various latency conditions for the purposes of experimenting with meta-data attributes relating to temporal events in the example life-cycle is developed. From this several online learner algorithmic and procedural approaches are tested as a potential solution to handling latency; based upon not just isolated examples but comprehension of the temporal nature of a data stream. Finally, future work is suggested for further improvements.
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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 using some voting scheme. In a traditional ensemble, the underlying base classifiers are all of the same type. Recent research extends the idea of ensemble learning to the idea of using committees, where a committee consists of diverse classifiers. This is the main difference between the regular ensemble classifiers and the committee learning algorithms. Committees are able to use diverse learning methods simultaneously and dynamically take advantage of the most accurate classifiers as the data change. In addition, some committees are able to replace their members when they perform poorly. This thesis presents two new algorithms that address concept drifts. The first algorithm has been designed to systematically introduce gradual and sudden concept drift scenarios into datasets. In order to save time and avoid memory consumption, the Concept Drift Introducer (CDI) algorithm divides the number of drift scenarios into phases. The main advantage of using phases is that it allows us to produce a highly scalable concept drift detector that evaluates each phase, instead of evaluating each individual drift scenario. We further designed a novel algorithm to handle concept drift. Our Dynamic Committee for Concept Drift (DCCD) algorithm uses a voted committee of hypotheses that vote on the best base classifier, based on its predictive accuracy. The novelty of DCCD lies in the fact that we employ diverse heterogeneous classifiers in one committee in an attempt to maximize diversity. DCCD detects concept drifts by using the accuracy and by weighing the committee members by adding one point to the most accurate member. The total loss in accuracy for each member is calculated at the end of each point of measurement, or phase. The performance of the committee members are evaluated to decide whether a member needs to be replaced or not. Moreover, DCCD detects the worst member in the committee and then eliminates this member by using a weighting mechanism. Our experimental evaluation centers on evaluating the performance of DCCD on various datasets of different sizes, with different levels of gradual and sudden concept drift. We further compare our algorithm to another state-of-the-art algorithm, namely the MultiScheme approach. The experiments indicate the effectiveness of our DCCD method under a number of diverse circumstances. The DCCD algorithm generally generates high performance results, especially when the number of concept drifts is large in a dataset. For the size of the datasets used, our results showed that DCCD produced a steady improvement in performance when applied to small datasets. Further, in large and medium datasets, our DCCD method has a comparable, and often slightly higher, performance than the MultiScheme technique. The experimental results also show that the DCCD algorithm limits the loss in accuracy over time, regardless of the size of the dataset.
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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 order to improve accuracy in changing environments. This is the first diversity study in the presence of concept drift. The main contributions of the thesis are: - An analysis of negative correlation in online learning. - A new concept drift categorisation to allow principled studies of drifts. - A better understanding of when, how and why ensembles of learning machines can help to handle concept drift in online learning. - Knowledge of how to use information learnt from the old concept to aid the learning of the new concept. - A new approach called Diversity for Dealing with Drifts (DDD), which is accurate both in the presence and absence of drifts.
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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-term interest (concept) models in an attempt to benefit from the strength of both models. The MTDR algorithm improves over existing concept drift learning algorithms in the domain. Being able to track multiple target concepts with a few examples poses an even more important and challenging problem because casual users tend to be reluctant to provide the examples needed, and learning from a few labeled data is generally difficult. The second part presents a computational Framework for Extending Incomplete Labeled Data Stream (FEILDS). The system modularly extends the capability of an existing concept drift learner in dealing with incomplete labeled data stream. It expands the learner's original input stream with relevant unlabeled data; the process generates a new stream with improved learnability. FEILDS employs a concept formation system for organizing its input stream into a concept (cluster) hierarchy. The system uses the concept and cluster hierarchy to identify the instance's concept and unlabeled data relevant to a concept. It also adopts the persistence assumption in temporal reasoning for inferring the relevance of concepts. Empirical evaluation indicates that FEILDS is able to improve the performance of existing learners particularly when learning from a stream with a few labeled data. Lastly, a new concept formation algorithm, one of the key components in the FEILDS architecture, is presented. The main idea is to discover intrinsic hierarchical structures regardless of the class distribution and the shape of the input stream. Experimental evaluation shows that the algorithm is relatively robust to input ordering, consistently producing a hierarchy structure of high quality.
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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
COORDENAÇÃO 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.
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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 (in terms of the degradation of the classifier’s performance). Given this, to achieve more effective concept drift management, we propose incorporating context awareness when adapting the classification model to changes. Explicit identification and monitoring of the contextual aspects enable capturing the causes of drift, and hence facilitating more proactive adaptation. In particular, we propose an information-theoretic-based approach for systematic context identification, aiming to learn from data the contextual characteristics of the domain of interest by identifying the context variables contributing to concept changes. Such characteristics are then utilised as important clues guiding the adaptation process of the classification model. Specifically, knowledge of contextual variables are exploited to select the most relevant data for retraining the model via a data weighting model, and to signal the need for data re-selection via a change detection model. The experimental analyses on simulated, benchmark, and real-world datasets, show that such explicit identification and utilisation of contextual information result in a more effective data selection and drift detection strategies, and enable to produce more accurate predictions.
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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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Books on the topic "Concept drift"

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Black, Michaela. Learning to classify from temporal data in the presence of concept drift and noise. [S.l: The author], 2002.

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Navajo 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.

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United States. National Park Service., ed. Environmental assessment: Draft development concept plan : Quinault Area. [Port Angeles, Wash.]: National Park Service, 1988.

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United 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.

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United 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.

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United 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.

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U.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.

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U.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.

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U.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.

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United 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.

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Book chapters on the topic "Concept drift"

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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.

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Sammut, 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.

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Sayed-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.

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Mattos, 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.

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Gulla, 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.

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Seraj, 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.

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Göpfert, 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.

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Scanlan, 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.

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Case, 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.

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Bose, 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.

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Conference papers on the topic "Concept drift"

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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.

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Dries, 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.

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YOSHIDA, 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.

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Almog, 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.

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Decision trees are commonly used in machine learning since they are accurate and robust classifiers. After a decision tree is built, the data can change over time, causing the classification performance to decrease. This data distribution change is a known challenge in machine learning, referred to as concept drift. Once a concept drift has been detected, usually by experiencing a decrease in the model's performance, it can be handled by training a new model. However, this method does not explain the drift harming the performance but only handles the drift's effects. The main contribution of this paper presents a novel two-step approach called APPETITE, which applies diagnosis techniques to identify the feature that has drifted and then adjusts the model accordingly. For the diagnosis step, we present two algorithms. We experimented on 73 known datasets from the literature and semi-synthesized drifts in their features. Both algorithms are better at handling concept drift than training a new model based on the samples after the drift. Combining the two algorithms can provide an explanation of the drift and is a competitive model against a new model trained on the entire data from before and after the drift.
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Liu, 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.

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In data stream mining, the emergence of new patterns or a pattern ceasing to exist is called concept drift. Concept drift makes the learning process complicated because of the inconsistency between existing data and upcoming data. Since concept drift was first proposed, numerous articles have been published to address this issue in terms of distribution analysis. However, most distribution-based drift detection methods assume that a drift happens at an exact time point, and the data arrived before that time point is considered not important. Thus, if a drift only occurs in a small region of the entire feature space, the other non-drifted regions may also be suspended, thereby reducing the learning efficiency of models. To retrieve non-drifted information from suspended historical data, we propose a local drift degree (LDD) measurement that can continuously monitor regional density changes. Instead of suspending all historical data after a drift, we synchronize the regional density discrepancies according to LDD. Experimental evaluations on three public data sets show that our concept drift adaptation algorithm improves accuracy compared to other methods.
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Bach, 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.

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Hinder, 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.

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Zhao, 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.

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Heng 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.

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Xu, 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.

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Reports on the topic "Concept drift"

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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.

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Tahmasbi, 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.

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S.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.

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A.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.

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Peters, 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.

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CFA Institute comments on the FASB exposure draft of the Proposed Statement of Financial Accounting Concepts - Concepts Statement No. 8, Conceptual Framework for Financial Reporting Chapter 6: Measurement.
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Booker, 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.

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TRANSPORTATION 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.

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Hugo, 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.

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Jacques 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.

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Greenberg, 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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