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Diversity-Based Pool of Models for Dealing with Recurring Concepts
2018
2018 International Joint Conference on Neural Networks (IJCNN)
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In particular, DDM and Entropy Measure were selected as the best-performing drift detection method and diversity measure respectively, for use with our proposed approach. ...
All strategies created a new model as representative after that drift detection. ...
doi:10.1109/ijcnn.2018.8489190
dblp:conf/ijcnn/ChiuM18
fatcat:lhfmkh6bhbgvfbpukrxadk5cdu
Ensemble Diversity in Evolving Data Streams
[chapter]
2016
Lecture Notes in Computer Science
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While diversity of ensembles has been studied in the context of static data, it has not still received such research interest for evolving data streams. ...
Measures highlighted as capable of depicting sudden and virtual drifts over time are used as basis for detecting changes with the Page-Hinkley test. ...
In this paper, we investigate ensemble diversity measures not only as a means of visualizing ensemble and stream characteristics, but also as a basis for drift detection. ...
doi:10.1007/978-3-319-46307-0_15
fatcat:gqfkvtyufrhw3eptlmm7carroi
Ensemble based on Accuracy and Diversity Weighting for Evolving Data Streams
2022
˜The œinternational Arab journal of information technology
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Based on MAD, a novel online ensemble method called Accuracy and Diversity weighted Ensemble (ADE) effectively handles concept drift in data streams. ...
In this paper, unlike conventional data-streams oriented ensemble methods, we propose a novel Measure via both Accuracy and Diversity (MAD) instead of one of them to supervise ensemble learning. ...
So, the ensemble classifier has a good generalization. 2) The measure of diversity In our algorithm, we adopt the Q statistic as the diversity measure. ...
doi:10.34028/iajit/19/1/11
fatcat:dldopmxl5zbs5dz6nucpqfspua
An Adaptive Heterogeneous Online Learning Ensemble Classifier for Nonstationary Environments
2021
Computational Intelligence and Neuroscience
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In such nonstationary environments, the underlying process generating the data stream is characterized by an intrinsic nonstationary or evolving or drifting phenomenon known as concept drift. ...
In recent years, the prevalence of technological advances has led to an enormous and ever-increasing amount of data that are now commonly available in a streaming fashion. ...
method (DDM) was used as a drift detection mechanism to test the drift handling capabilities. ...
doi:10.1155/2021/6669706
pmid:33815495
pmcid:PMC7987417
fatcat:xaw4dznl2bgzbbog7iq2v7yu3a
ADES: A New Ensemble Diversity-Based Approach for Handling Concept Drift
2021
Mobile Information Systems
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at any time and improves convergence to new concepts by exploiting different amounts of ensemble diversity. ...
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 ...
A change detection algorithm monitors the predictions of the low diversity ensemble and when a concept drift is detected by the measure of a drop in prediction of the subsequent data chunk, the drift detection ...
doi:10.1155/2021/5549300
fatcat:7djzzsolineg7duijijn2yuy64
Fast Reaction to Sudden Concept Drift in the Absence of Class Labels
2020
Applied Sciences
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A data stream can be considered as a sequence of examples that arrive continuously and are potentially unbounded, such as web page visits, sensor readings and call records. ...
One of the serious and challenging problems that appears in a data stream is concept drift. This problem occurs when the relation between the input data and the target variable changes over time. ...
First, we propose a drift detector based on a Diversity Measure as a Drift Detection Method (DMDDM) to detect concept drift in a semisupervised environment (DMDDM-S). ...
doi:10.3390/app10020606
fatcat:6syml5vzirhbpcbrv2cbdmr23u
Classification systems in dynamic environments: an overview
2016
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
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In addition, we provide the definitions of the main terms used to represent this problem in the literature, such as concept drift and novelty detection. ...
Currently, in the literature, the most common strategies used to detect changes are based on accuracy monitoring, which relies on previous knowledge of the data in order to identify whether or not correct ...
MAIN EVENTS IN DATA STREAMS As in data streams arrive a massive quantity of examples, it is very common that the occurrence of events become a challenge in the classification tasks. ...
doi:10.1002/widm.1184
fatcat:mjmtv65vmfeblakxyqwpcfuok4
Ensemble learning for data stream analysis: A survey
2017
Information Fusion
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Besides presenting a comprehensive spectrum of ensemble approaches for data streams, we also discuss advanced learning concepts such as imbalanced data streams, novelty detection, active and semisupervised ...
This paper surveys research on ensembles for data stream classification as well as regression tasks. ...
Section 2 focuses on the main characteristics of data streams and methods dedicated to their analysis, as well as on the type of data streams and drift detection methods. ...
doi:10.1016/j.inffus.2017.02.004
fatcat:rfc735znxjcwdebcbjxbyx7xki
Diversity of Ensembles for Data Stream Classification
[article]
2019
arXiv
pre-print
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However, diversity measures have not received much research interest in evolving data streams. ...
In this paper, we present a theoretical analysis of different diversity measures and relate them to the success of ensemble learning algorithms for streaming data. ...
In the next section, we present a few diversity measuring techniques, but this time in a data stream setting.
B. ...
arXiv:1902.08466v1
fatcat:pa5eqgdwt5a5libyeidvgm6qdm
Empirical Study of Impact of Various Concept Drifts in Data Stream Mining Methods
2016
International Journal of Intelligent Systems and Applications
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As a result, the concept drifts occur very frequently in the data stream. ...
Index Terms-Concept drift, on line learn ing, data stream mining, drift detection, ensembles. ...
Drift Detection Method (DDM) Drift Detection Method (DDM ) [11] , as mentioned above is based on drift detection, when a drift is detected the system rebuild itself to incorporate the change in the concept ...
doi:10.5815/ijisa.2016.12.08
fatcat:kfdiokpojnfbzlf3soe2qltxae
A Diversity Framework for Dealing With Multiple Types of Concept Drift Based on Clustering in the Model Space
2020
IEEE Transactions on Neural Networks and Learning Systems
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We propose to use diversity as a framework to handle multiple types of drift. ...
Data stream applications usually suffer from multiple types of concept drift. ...
The diversity measure used in DP is in {Entropy Measure, Q-Statistic}. ...
doi:10.1109/tnnls.2020.3041684
pmid:33351764
fatcat:znfm7ipvibgirmh62btrzb4tou
Design of adaptive ensemble classifier for online sentiment analysis and opinion mining
2021
PeerJ Computer Science
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The drift detection algorithm needs to be very sensitive to change in data distribution for detecting the maximum number of drifts in the data stream. ...
DataStream mining is a challenging task for researchers because of the change in data distribution during classification, known as concept drift. ...
This method performs well with standard data streams, but it has a weakness, as this method uses DDM for drift detection, which is an error-based drift detection method and cannot work well when the data ...
doi:10.7717/peerj-cs.660
fatcat:yojujw7tbnde5jm2e5d5vxbcpy
Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts
2021
Applied Computational Intelligence and Soft Computing
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For most real-world data streams, the concept about which data is obtained may shift from time to time, a phenomenon known as concept drift. ...
For most real-world applications such as nonstationary time-series data, concept drift often occurs in a cyclic fashion, and previously seen concepts will reappear, which supports a unique kind of concept ...
Input: (D 1, D 2, . . . .., D t ) the streaming data chunks E t archive of ensemble models at time step t Diversity measure : Q Statistic Drift Detection Method Detect Drift Output: F t : the generalized ...
doi:10.1155/2021/5533777
fatcat:tfnimgu2zjd4lbc7wimou22qpa
Aspect Oriented Concept Drift Detection in High Dimensional Data Streams
2020
International Journal of Advanced Trends in Computer Science and Engineering
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In this document, aspect-oriented concept drift detection (AOCDD) is projected into high-dimension data streams. ...
To report concept drift, the AOCDD represents the diversity of data projection for the aspects that are used to frame the record structure in the target data streams. ...
Then take a twice as many records as a new window cw. ...
doi:10.30534/ijatcse/2020/111922020
fatcat:iz2hmg7d4vduvltedlqmbz7bii
Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams
2018
Machine Learning
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In addition, a number of methods have been developed to detect concept drifts in these streams. ...
We introduce the Tornado framework that implements a reservoir of diverse classifiers, together with a variety of drift detection algorithms. ...
The authors further acknowledge funding by the Canadian Natural Sciences and Engineering Research Council (NSERC) as well as the Ontario Trillium Scholarship (OTS). ...
doi:10.1007/s10994-018-5719-z
fatcat:lkeartys4nfo3ko7epdwaxlksy
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