Learning From Data Streams In Evolving Environments

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Learning From Data Streams In Evolving Environments

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Author : Moamar Sayed-Mouchaweh
language : en
Publisher: Springer
Release Date : 2018-07-28

Learning From Data Streams In Evolving Environments written by Moamar Sayed-Mouchaweh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-28 with Technology & Engineering categories.

This edited book covers recent advances of techniques, methods and tools treating the problem of learning from data streams generated by evolving non-stationary processes. The goal is to discuss and overview the advanced techniques, methods and tools that are dedicated to manage, exploit and interpret data streams in non-stationary environments. The book includes the required notions, definitions, and background to understand the problem of learning from data streams in non-stationary environments and synthesizes the state-of-the-art in the domain, discussing advanced aspects and concepts and presenting open problems and future challenges in this field. Provides multiple examples to facilitate the understanding data streams in non-stationary environments; Presents several application cases to show how the methods solve different real world problems; Discusses the links between methods to help stimulate new research and application directions.

Learning From Data Streams In Dynamic Environments

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Author : Moamar Sayed-Mouchaweh
language : en
Publisher: Springer
Release Date : 2015-12-10

Learning From Data Streams In Dynamic Environments written by Moamar Sayed-Mouchaweh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-10 with Technology & Engineering categories.

This book addresses the problems of modeling, prediction, classification, data understanding and processing in non-stationary and unpredictable environments. It presents major and well-known methods and approaches for the design of systems able to learn and to fully adapt its structure and to adjust its parameters according to the changes in their environments. Also presents the problem of learning in non-stationary environments, its interests, its applications and challenges and studies the complementarities and the links between the different methods and techniques of learning in evolving and non-stationary environments.

Intelligent Data Engineering And Automated Learning

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Author :
language : en
Publisher:
Release Date : 2004

Intelligent Data Engineering And Automated Learning written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Data mining categories.

3rd Ieee International Conference On Data Mining

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Author : Xindong Wu
language : en
Publisher: Institute of Electrical & Electronics Engineers(IEEE)
Release Date : 2003

3rd Ieee International Conference On Data Mining written by Xindong Wu and has been published by Institute of Electrical & Electronics Engineers(IEEE) this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Computers categories.

ICDM '03 brings together researchers and practitioners who describe their original research results and practical development experiences in Data Mining technology. The papers explore subjects in many related data-mining areas such as machine learning, automated scientific discovery, statistics, pattern recognition, knowledge acquisition, soft computing, databases, data warehousing, data visualization, and knowledge-based systems. Data mining is an emerging and highly interdisciplinary field. The ICDM '03 proceedings cover a broad and diverse range of topics related to data-mining theory, systems, and applications.

A Reservoir Of Adaptive Algorithms For Online Learning From Evolving Data Streams

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Author : Ali Pesaranghader
language : en
Publisher:
Release Date : 2018

A Reservoir Of Adaptive Algorithms For Online Learning From Evolving Data Streams written by Ali Pesaranghader and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.

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 performance of learning algorithms due to the nature of incoming data as it continuously evolves. That is, the current efficient learning approach may become deprecated after a change in data or environment. Hence, the question 'how to have an efficient learning algorithm over time against evolving data?' has to be addressed. In this thesis, we have made two contributions to settle the challenges described above. In the machine learning literature, the phenomenon of (distributional) change in data is known as concept drift. Concept drift may shift decision boundaries, and cause a decline in accuracy. Learning algorithms, indeed, have to detect concept drift in evolving data streams and replace their predictive models accordingly. To address this challenge, adaptive learners have been devised which may utilize drift detection methods to locate the drift points in dynamic and changing data streams. A drift detection method able to discover the drift points quickly, with the lowest false positive and false negative rates, is preferred. False positive refers to incorrectly alarming for concept drift, and false negative refers to not alarming for concept drift. In this thesis, we introduce three algorithms, called as the Fast Hoeffding Drift Detection Method (FHDDM), the Stacking Fast Hoeffding Drift Detection Method (FHDDMS), and the McDiarmid Drift Detection Methods (MDDMs), for detecting drift points with the minimum delay, false positive, and false negative rates. FHDDM is a sliding window-based algorithm and applies Hoeffding's inequality (Hoeffding, 1963) to detect concept drift. FHDDM slides its window over the prediction results, which are either 1 (for a correct prediction) or 0 (for a wrong prediction). Meanwhile, it compares the mean of elements inside the window with the maximum mean observed so far; subsequently, a significant difference between the two means, upper-bounded by the Hoeffding inequality, indicates the occurrence of concept drift. The FHDDMS extends the FHDDM algorithm by sliding multiple windows over its entries for a better drift detection regarding the detection delay and false negative rate. In contrast to FHDDM/S, the MDDM variants assign weights to their entries, i.e., higher weights are associated with the most recent entries in the sliding window, for faster detection of concept drift. The rationale is that recent examples reflect the ongoing situation adequately. Then, by putting higher weights on the latest entries, we may detect concept drift quickly. An MDDM algorithm bounds the difference between the weighted mean of elements in the sliding window and the maximum weighted mean seen so far, using McDiarmid's inequality (McDiarmid, 1989). Eventually, it alarms for concept drift once a significant difference is experienced. We experimentally show that FHDDM/S and MDDMs outperform the state-of-the-art by representing promising results in terms of the adaptation and classification measures. Due to the evolving nature of data streams, the performance of an adaptive learner, which is defined by the classification, adaptation, and resource consumption measures, may fluctuate over time. In fact, a learning algorithm, in the form of a (classifier, detector) pair, may present a significant performance before a concept drift point, but not after. We define this problem by the question 'how can we ensure that an efficient classifier-detector pair is present at any time in an evolving environment?' To answer this, we have developed the Tornado framework which runs various kinds of learning algorithms simultaneously against evolving data streams. Each algorithm incrementally and independently trains a predictive model and updates the statistics of its drift detector. Meanwhile, our framework monitors the (classifier, detector) pairs, and recommends the efficient one, concerning the classification, adaptation, and resource consumption performance, to the user. We further define the holistic CAR measure that integrates the classification, adaptation, and resource consumption measures for evaluating the performance of adaptive learning algorithms. Our experiments confirm that the most efficient algorithm may differ over time because of the developing and evolving nature of data streams.

Proceedings Of The Seventh Siam International Conference On Data Mining

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Author : Chid Apte
language : en
Publisher: Society for Industrial and Applied Mathematics (SIAM)
Release Date : 2007

Proceedings Of The Seventh Siam International Conference On Data Mining written by Chid Apte and has been published by Society for Industrial and Applied Mathematics (SIAM) this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computers categories.

The Seventh SIAM International Conference on Data Mining (SDM 2007) continues a series of conferences whose focus is the theory and application of data mining to complex datasets in science, engineering, biomedicine, and the social sciences. These datasets challenge our abilities to analyze them because they are large and often noisy. Sophisticated, highperformance, and principled analysis techniques and algorithms, based on sound statistical foundations, are required. Visualization is often critically important; tuning for performance is a significant challenge; and the appropriate levels of abstraction to allow end-users to exploit sophisticated techniques and understand clearly both the constraints and interpretation of results are still something of an open question.

An Integrated Approach To Autonomous Computation In Data Streaming Applications

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Author : Eric P. Kasten
language : en
Publisher:
Release Date : 2007

An Integrated Approach To Autonomous Computation In Data Streaming Applications written by Eric P. Kasten and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Autonomic computing categories.

Kdd

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Author :
language : en
Publisher:
Release Date : 2003

Kdd written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Data mining categories.

Advances In Knowledge Discovery And Data Mining

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Author :
language : en
Publisher:
Release Date : 2005

Advances In Knowledge Discovery And Data Mining written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Data mining categories.

Science Technology Review

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Author :
language : en
Publisher:
Release Date : 2013

Science Technology Review written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Military research categories.