Готові списки джерел за темами / Concept-Drift Management

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Автор: Grafiati
Опубліковано: 14 березня 2023

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Статті в журналах з теми "Concept-Drift Management"

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Cano, Andrés, Manuel Gómez-Olmedo, and Serafín Moral. "A Bayesian approach to abrupt concept drift." Knowledge-Based Systems 185 (December 2019): 104909. http://dx.doi.org/10.1016/j.knosys.2019.104909.

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Bayram, Firas, Bestoun S. Ahmed, and Andreas Kassler. "From concept drift to model degradation: An overview on performance-aware drift detectors." Knowledge-Based Systems 245 (June 2022): 108632. http://dx.doi.org/10.1016/j.knosys.2022.108632.

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Elkhawaga, Ghada, Mervat Abuelkheir, Sherif I. Barakat, Alaa M. Riad, and Manfred Reichert. "CONDA-PM—A Systematic Review and Framework for Concept Drift Analysis in Process Mining." Algorithms 13, no. 7 (July 3, 2020): 161. http://dx.doi.org/10.3390/a13070161.

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Анотація:
Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it. The situation when a business process changes while being analysed is denoted as Concept Drift. Its analysis is concerned with studying how a business process changes, in terms of detecting and localising changes and studying the effects of the latter. Concept drift analysis is crucial to enable early detection and management of changes, that is, whether to promote a change to become part of an improved process, or to reject the change and make decisions to mitigate its effects. Despite its importance, there exists no comprehensive framework for analysing concept drift types, affected process perspectives, and granularity levels of a business process. This article proposes the CONcept Drift Analysis in Process Mining (CONDA-PM) framework describing phases and requirements of a concept drift analysis approach. CONDA-PM was derived from a Systematic Literature Review (SLR) of current approaches analysing concept drift. We apply the CONDA-PM framework on current approaches to concept drift analysis and evaluate their maturity. Applying CONDA-PM framework highlights areas where research is needed to complement existing efforts.
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Zheng, Xiulin, Peipei Li, Xuegang Hu, and Kui Yu. "Semi-supervised classification on data streams with recurring concept drift and concept evolution." Knowledge-Based Systems 215 (March 2021): 106749. http://dx.doi.org/10.1016/j.knosys.2021.106749.

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Mwitondi, Kassim S., and Raed A. Said. "Dealing with Randomness and Concept Drift in Large Datasets." Data 6, no. 7 (July 19, 2021): 77. http://dx.doi.org/10.3390/data6070077.

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Анотація:
Data-driven solutions to societal challenges continue to bring new dimensions to our daily lives. For example, while good-quality education is a well-acknowledged foundation of sustainable development, innovation and creativity, variations in student attainment and general performance remain commonplace. Developing data -driven solutions hinges on two fronts-technical and application. The former relates to the modelling perspective, where two of the major challenges are the impact of data randomness and general variations in definitions, typically referred to as concept drift in machine learning. The latter relates to devising data-driven solutions to address real-life challenges such as identifying potential triggers of pedagogical performance, which aligns with the Sustainable Development Goal (SDG) #4-Quality Education. A total of 3145 pedagogical data points were obtained from the central data collection platform for the United Arab Emirates (UAE) Ministry of Education (MoE). Using simple data visualisation and machine learning techniques via a generic algorithm for sampling, measuring and assessing, the paper highlights research pathways for educationists and data scientists to attain unified goals in an interdisciplinary context. Its novelty derives from embedded capacity to address data randomness and concept drift by minimising modelling variations and yielding consistent results across samples. Results show that intricate relationships among data attributes describe the invariant conditions that practitioners in the two overlapping fields of data science and education must identify.
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Cabral, Danilo Rafael de Lima, and Roberto Souto Maior de Barros. "Concept drift detection based on Fisher’s Exact test." Information Sciences 442-443 (May 2018): 220–34. http://dx.doi.org/10.1016/j.ins.2018.02.054.

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Barros, Roberto Souto Maior, and Silas Garrido T. Carvalho Santos. "A large-scale comparison of concept drift detectors." Information Sciences 451-452 (July 2018): 348–70. http://dx.doi.org/10.1016/j.ins.2018.04.014.

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Delany, Sarah Jane, Pádraig Cunningham, Alexey Tsymbal, and Lorcan Coyle. "A case-based technique for tracking concept drift in spam filtering." Knowledge-Based Systems 18, no. 4-5 (August 2005): 187–95. http://dx.doi.org/10.1016/j.knosys.2004.10.002.

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Li, Yanhong, Deyu Li, Suge Wang, and Yanhui Zhai. "Incremental entropy-based clustering on categorical data streams with concept drift." Knowledge-Based Systems 59 (March 2014): 33–47. http://dx.doi.org/10.1016/j.knosys.2014.02.004.

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Thaipisutikul, Tipajin. "An Adaptive Temporal-Concept Drift Model for Sequential Recommendation." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 16, no. 2 (June 11, 2022): 222–36. http://dx.doi.org/10.37936/ecti-cit.2022162.248019.

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Анотація:
Recently, owing to the great advances in Web 2.0 and mobile devices, various online commercial services have emerged. Recommendation systems play an important role in dealing with abundant product information from massive numbers of online e-commerce transactions. Providing an accurate recommendation at the correct time to customers can contribute to a surge in business success. In this paper, an adaptive temporal-concept drift learning-based recommendation system, ATCRec, is developed for precisely tackling the sequential recommendation problem. We embed sequences of items into the latent spaces and learn both general preferences and sequential patterns concurrently via a recurrent neural network. Specifically, ATCRec captures dynamic changes in the temporal and concept drift contexts by modifying the gate units in a traditional recurrent neural network. The proposed model provides a unified and flexible network structure to learn and reveal the opaque variation of user preferences over time. We evaluate the robustness and performance of ATCRec on two real-world datasets, and the experimental results demonstrate that ATCRec consistently outperforms existing sequential recommendation approaches on various metrics. This indicates that integrating users' temporal information and concept drift variation through time are indispensable in improving the performance of recommendation systems.
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Більше джерел

Дисертації з теми "Concept-Drift Management"

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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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Анотація:
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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Частини книг з теми "Concept-Drift Management"

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Patil, Malini M. "Handling Concept Drift in Data Streams by Using Drift Detection Methods." In Data Management, Analytics and Innovation, 155–66. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1274-8_12.

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Woźniak, Michał, Paweł Ksieniewicz, Bogusław Cyganek, and Krzysztof Walkowiak. "Ensembles of Heterogeneous Concept Drift Detectors - Experimental Study." In Computer Information Systems and Industrial Management, 538–49. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45378-1_48.

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Wang, Shenghui, Stefan Schlobach, and Michel Klein. "What Is Concept Drift and How to Measure It?" In Knowledge Engineering and Management by the Masses, 241–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16438-5_17.

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Marrs, Gary R., Ray J. Hickey, and Michaela M. Black. "The Impact of Latency on Online Classification Learning with Concept Drift." In Knowledge Science, Engineering and Management, 459–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15280-1_42.

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Yang, Ming, William H. Hsu, and Surya Teja Kallumadi. "Predictive Analytics of Social Networks." In Advances in Data Mining and Database Management, 297–333. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5063-3.ch013.

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Анотація:
In this chapter, the authors survey the general problem of analyzing a social network in order to make predictions about its behavior, content, or the systems and phenomena that generated it. They begin by defining five basic tasks that can be performed using social networks: (1) link prediction; (2) pathway and community formation; (3) recommendation and decision support; (4) risk analysis; and (5) planning, especially causal interventional planning. Next, they discuss frameworks for using predictive analytics, availability of annotation, text associated with (or produced within) a social network, information propagation history (e.g., upvotes and shares), trust, and reputation data. They also review challenges such as imbalanced and partial data, concept drift especially as it manifests within social media, and the need for active learning, online learning, and transfer learning. They then discuss general methodologies for predictive analytics involving network topology and dynamics, heterogeneous information network analysis, stochastic simulation, and topic modeling using the abovementioned text corpora. They continue by describing applications such as predicting “who will follow whom?” in a social network, making entity-to-entity recommendations (person-to-person, business-to-business [B2B], consumer-to-business [C2B], or business-to-consumer [B2C]), and analyzing big data (especially transactional data) for Customer Relationship Management (CRM) applications. Finally, the authors examine a few specific recommender systems and systems for interaction discovery, as part of brief case studies.
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Yang, Ming, William H. Hsu, and Surya Teja Kallumadi. "Predictive Analytics of Social Networks." In Business Intelligence, 1080–116. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9562-7.ch056.

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Анотація:
In this chapter, the authors survey the general problem of analyzing a social network in order to make predictions about its behavior, content, or the systems and phenomena that generated it. They begin by defining five basic tasks that can be performed using social networks: (1) link prediction; (2) pathway and community formation; (3) recommendation and decision support; (4) risk analysis; and (5) planning, especially causal interventional planning. Next, they discuss frameworks for using predictive analytics, availability of annotation, text associated with (or produced within) a social network, information propagation history (e.g., upvotes and shares), trust, and reputation data. They also review challenges such as imbalanced and partial data, concept drift especially as it manifests within social media, and the need for active learning, online learning, and transfer learning. They then discuss general methodologies for predictive analytics involving network topology and dynamics, heterogeneous information network analysis, stochastic simulation, and topic modeling using the abovementioned text corpora. They continue by describing applications such as predicting “who will follow whom?” in a social network, making entity-to-entity recommendations (person-to-person, business-to-business [B2B], consumer-to-business [C2B], or business-to-consumer [B2C]), and analyzing big data (especially transactional data) for Customer Relationship Management (CRM) applications. Finally, the authors examine a few specific recommender systems and systems for interaction discovery, as part of brief case studies.
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7

Yang, Ming, William H. Hsu, and Surya Teja Kallumadi. "Predictive Analytics of Social Networks." In Social Media Marketing, 823–62. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5637-4.ch042.

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Анотація:
In this chapter, the authors survey the general problem of analyzing a social network in order to make predictions about its behavior, content, or the systems and phenomena that generated it. They begin by defining five basic tasks that can be performed using social networks: (1) link prediction; (2) pathway and community formation; (3) recommendation and decision support; (4) risk analysis; and (5) planning, especially causal interventional planning. Next, they discuss frameworks for using predictive analytics, availability of annotation, text associated with (or produced within) a social network, information propagation history (e.g., upvotes and shares), trust, and reputation data. They also review challenges such as imbalanced and partial data, concept drift especially as it manifests within social media, and the need for active learning, online learning, and transfer learning. They then discuss general methodologies for predictive analytics involving network topology and dynamics, heterogeneous information network analysis, stochastic simulation, and topic modeling using the abovementioned text corpora. They continue by describing applications such as predicting “who will follow whom?” in a social network, making entity-to-entity recommendations (person-to-person, business-to-business [B2B], consumer-to-business [C2B], or business-to-consumer [B2C]), and analyzing big data (especially transactional data) for Customer Relationship Management (CRM) applications. Finally, the authors examine a few specific recommender systems and systems for interaction discovery, as part of brief case studies.
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Тези доповідей конференцій з теми "Concept-Drift Management"

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Mak, Lee-onn, and Paul Krause. "Detection & Management of Concept Drift." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258538.

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Gözüaçık, Ömer, Alican Büyükçakır, Hamed Bonab, and Fazli Can. "Unsupervised Concept Drift Detection with a Discriminative Classifier." In CIKM '19: The 28th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3357384.3358144.

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Balog, Katalin. "The Concept and Competitiveness of Agile Organization in the Fourth Industrial Revolution’s Drift." In 25th International Scientific Conference Strategic Management and Decision Support Systems in Strategic Management. University of Novi Sad, Faculty of Economics in Subotica, 2020. http://dx.doi.org/10.46541/978-86-7233-386-2_5.

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4

Seeliger, Alexander, Timo Nolle, and Max Mühlhäuser. "Detecting Concept Drift in Processes using Graph Metrics on Process Graphs." In S-BPM ONE '17: Conference on Subject-orientied Business Process Management. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3040565.3040566.

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Tian, Hongda, Nguyen Lu Dang Khoa, Ali Anaissi, Yang Wang, and Fang Chen. "Concept Drift Adaption for Online Anomaly Detection in Structural Health Monitoring." In CIKM '19: The 28th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3357384.3357816.

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6

Saurav, Sakti, Pankaj Malhotra, Vishnu TV, Narendhar Gugulothu, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff. "Online anomaly detection with concept drift adaptation using recurrent neural networks." In CoDS-COMAD '18: The ACM India Joint International Conference on Data Science & Management of Data. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3152494.3152501.

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7

Henke, Marcia, Eduardo Souto, and Eulanda M. dos Santos. "Analysis of the evolution of features in classification problems with concept drift: Application to spam detection." In 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM). IEEE, 2015. http://dx.doi.org/10.1109/inm.2015.7140398.

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8

So¨derkvist, Johan, and Tomas Jansson. "IceMS: A Software for Ice Management." In ASME 2005 24th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2005. http://dx.doi.org/10.1115/omae2005-67516.

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Анотація:
A combined system consisting of a forecast model to predict ice drift and a viewer tool for ice management, IceMS were developed for providing ice information during the Arctic Coring EXpedition (ACEX). The main task during the multi international expedition ACEX was to retrieve sediment cores from the Lomonosov Ridge in the central Arctic Ocean. Large ice floes or thick ice that drifts towards the drill ship threaten the operation by forcing the drill ship from its position. The gathered ice information was imported into IceMS and presented to the ice management team. Risk analysis for continued drilling operation was made, and instructions of where the Icebreakers Oden and Sovjetski Soyuz should break ice in the drilling area were carried out. The software IceMS contains tools to update the ice conditions, history lines of ships and buoys, and includes the possibility to import track lines showing the ice drift forecasts. The key concept is the combined visualization of map data and up to date imagery from satellite, airplane photos and ice charts, together with results from an ice drift forecast model. It is possible to move the images in IceMS map according to the observed ice drift recorded by the buoys placed on drifting ice floes. An overlay to mark and edit polygons, e.g. representing areas with certain ice classification, can be shown on top of the images. The edit overlay can be exported to file, which enables sharing of judgments and forecasts to other units. The model to predict ice drift is a state of the art ice drift model that is developed for describing rapid changes such as circular motion with a period of about twelve hours called inertial motion. The ice drift forecast was based on weather forecast and measured ocean currents near the drill site. The combined system of IceMS and the forecast model is being further developed for supporting ice management teams on offshore platforms and other constructions in ice infested areas. Results from Ice Management will be presented, showing examples of how IceMS presented ice information and validation of the ice drift model during the Arctic Coring EXpedition (ACEX). New tools in IceMS will also be presented.
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C. Lemos Neto, Álvaro, Rodrigo A. Coelho, and Cristiano L. de Castro. "An Incremental Learning approach using Long Short-Term Memory Neural Networks." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1491.

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Анотація:
Due to Big Data and the Internet of Things, Machine Learning algorithms targeted specifically to model evolving data streams had gained attention from both academia and industry. Many Incremental Learning models had been successful in doing so, but most of them have one thing in common: they are complex variants of batch learning algorithms, which is a problem since, in a streaming setting, less complexity and more performance is desired. This paper proposes the Incremental LSTM model, which is a variant of the original LSTM with minor changes, that can tackle evolving data streams problems such as concept drift and the elasticity-plasticity dilemma without neither needing a dedicated drift detector nor a memory management system. It obtained great results that show it reacts fast to concept drifts and that is also robust to noise data.
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Mo, Jia-hui, Peng Zou, and Jin Chen. "Context of the concept drift in data mining: An empirical study on the regional economic influence to the relation between demographic attributes and credit card holder’s loyalty." In 2008 International Conference on Management Science and Engineering (ICMSE). IEEE, 2008. http://dx.doi.org/10.1109/icmse.2008.4668891.

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