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Paired Learners for Concept Drift

Stephen H. Bach, Marcus A. Maloof
2008 2008 Eighth IEEE International Conference on Data Mining  
To cope with concept drift, we paired a stable online learner with a reactive one.  ...  does the reactive learner when the target concept changes.  ...  The authors thank Clay Shields and the anonymous reviewers for helpful comments on earlier drafts of this paper.  ... 
doi:10.1109/icdm.2008.119 dblp:conf/icdm/BachM08 fatcat:qqzb7xlxubcvllwlzsaz5n2qfa

A Paired Learner-Based Approach for Concept Drift Detection and Adaptation in Software Defect Prediction

Arvind Kumar Gangwar, Sandeep Kumar, Alok Mishra
2021 Applied Sciences  
Motivated from this observation, this paper presents a paired learner-based drift detection and adaptation approach in SDP that dynamically adapts the varying concepts by updating one of the learners in  ...  For a given defect dataset, a subset of data modules is analyzed at a time by both learners based on their learning experience from the past.  ...  The research in these studies did not provide any solution for handling concept drift in SDP, and they did not explore paired learner for CD adaptation in SDP.  ... 
doi:10.3390/app11146663 fatcat:4jd4etqtv5aajaxmgjgopxtngm

Just-In-Time Classifiers for Recurrent Concepts

C. Alippi, G. Boracchi, M. Roveri
2013 IEEE Transactions on Neural Networks and Learning Systems  
(like in paired learners) The ensemble is meant to improve reaction promptness to concept drift.  ...  = ( , , ) • : set of supervised samples • : set of features for assessing concept equivalence • : set of features for detecting concept drift Operators for Conceptsconcept-drift  ... 
doi:10.1109/tnnls.2013.2239309 pmid:24808382 fatcat:z76bjbl4gjbc7ggtadajwyrusm

Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams

Ali Pesaranghader, Herna Viktor, Eric Paquet
2018 Machine Learning  
In addition, a number of methods have been developed to detect concept drifts in these streams.  ...  Further, our FHDDMS variants detect concept drifts accurately in a timely fashion while outperforming the state-of-the-art.  ...  Finally, we wish to thank the anonymous reviewers for their invaluable feedback, that led us to improve this paper considerably.  ... 
doi:10.1007/s10994-018-5719-z fatcat:lkeartys4nfo3ko7epdwaxlksy

Review paper on adapting data stream mining concept drift using ensemble classifier approach

Nilima Motghare, Arvind Mewada
2014 IOSR Journal of Computer Engineering  
Bach et. al [14] proposed a paired learner that pairs a stable online learner with a reactive one to cope with concept drift.  ...  Tracking concept drift is important for many applications.  ... 
doi:10.9790/0661-1654120123 fatcat:wyub5nwbgbfzpgwo5x3hbxwski

Learning Calibration Functions on the Fly: Hybrid Batch Online Stacking Ensembles for the Calibration of Low-Cost Air Quality Sensor Networks in the Presence of Concept Drift

Evangelos Bagkis, Theodosios Kassandros, Kostas Karatzas
2022 Atmosphere  
Furthermore, we introduce the concept of spatial online learning to achieve better spatial generalization.  ...  The O3 LCS are successfully calibrated for 8–10 months and the PM10 LCS calibration is evaluated for 13–24 months showing a strong seasonal dependence on their ability to correctly capture the pollution  ...  Concept Drift Definition and Detection The formal way of defining a concept drift is by employing Bayes' law.  ... 
doi:10.3390/atmos13030416 fatcat:5t754gqngzf73gd53gnuamsn4e

A heterogeneous online learning ensemble for non-stationary environments

Mobin M. Idrees, Leandro L. Minku, Frederic Stahl, Atta Badii
2019 Knowledge-Based Systems  
., dealing with concept drift.  ...  Also, when recurring concept drifts were present, the predictive performance of HDWM showed an improvement over DWM.  ...  They use concept drift detection methods to determine whether a concept drift has occurred. When concept drift detection occurs, methods for dealing with concept drift are triggered.  ... 
doi:10.1016/j.knosys.2019.104983 fatcat:hrmfhkudovbz7bke3gppcsgdke

Generalized Cmac Adaptive Ensembles For Concept-Drifting Data Streams

Anibal Figueiras-Vidal, Francisco Javier Gonzalez Serrano
2018 Zenodo  
When pairs of base learners tend to agree, the ensemble is considered less diverse.  ...  (b) Low SeverityFrom Fig. 5 : 5 Evolution of β(t) for a High Speed concept drift. Fig. 6 : 6 Evolution of the prequential accuracy for a Low Speed concept drift. (a) High Severity.  ... 
doi:10.5281/zenodo.1159211 fatcat:qx4bbfaquzb3zdtv7owii5u7o4

Department of Computer Sciences: [chapter]

2019 First in the Field  
In particular, the adversary can change the target concept at any time (called a concept drift), unbeknownst to the learner.  ...  The WCST tests for cognitive "set shifting" (i.e. the readiness to abandon an old learned concept after a concept drift), which is difficult for human patients with pathology in the frontal lobes of the  ...  Paired t-tests at 0.05 level do not reveal significant differences between the number of trials for the four concepts: the monkeys do not seem to get better at concept drifts. m-trials m-errors m-presv  ... 
doi:10.2307/j.ctv15wxp84.13 fatcat:bmeagf5i4ndmxh4vnop2plp5jq

From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors [article]

Firas Bayram, Bestoun S. Ahmed, Andreas Kassler
2022 arXiv   pre-print
Different terms have been used in the literature to refer to the same type of concept drift and the same term for various types.  ...  We also review and classify performance-based concept drift detection methods proposed in the last decade.  ...  Acknowledgement Parts of this work has been funded by the Knowledge Foundation of Sweden (KKS) through the Synergy Project AIDA -A Holistic AI-driven Networking and Processing Framework for Industrial  ... 
arXiv:2203.11070v1 fatcat:wtndeywfhbc53galz47nnn6seu

Load Forecasting Under Concept Drift: Online Ensemble Learning with Recurrent Neural Network and ARIMA

Rashpinder Kaur Jagait, Mohammad Navid Fekri, Katarina Grolinger, Syed Mir
2021 IEEE Access  
For both window sizes, the errors are low before and after the concept drift, and the highest spikes are observed during the concept drift.  ...  At the start of the concept drift, the spikes are higher for the window size of 300 than for 200. With a large window size, the model needs more time to adjust to the concept drift.  ... 
doi:10.1109/access.2021.3095420 fatcat:mfqmsrlpn5dybkdqgvew4ceuk4

LUNAR: Cellular Automata for Drifting Data Streams [article]

Jesus L. Lobo, Javier Del Ser, Francisco Herrera
2020 arXiv   pre-print
It is able to act as a real incremental learner while adapting to drifting conditions.  ...  In addition, they can be affected by non-stationary phenomena (concept drift), by which learning methods have to detect changes in the distribution of streaming data, and adapt to these evolving conditions  ...  As shown in Algorithm 2 (Paired learning for OLMs), the stable learner is used for prediction, whereas the reactive learner is used to detect possible concept drifts: • While the concept remains unchanged  ... 
arXiv:2002.02164v1 fatcat:zmts7yqjvvhuhdj74cy2c4facu

Classification of Partially Labeled Malicious Web Traffic in the Presence of Concept Drift

Goce Anastasovski, Katerina Goseva Popstojanova
2014 2014 IEEE Eighth International Conference on Software Security and Reliability-Companion  
In this paper an incremental semi-supervised algorithm (CSL-Stream) is used to classify malicious Web traffic to multiple classes, as well as to analyze the concept drift and concept evolution phenomena  ...  (i.e., concept drift).  ...  concept drift in the data.  ... 
doi:10.1109/sere-c.2014.31 dblp:conf/ssiri/AnastasovskiG14 fatcat:5rm3xuewkndnre4gvpnzenubre

Diversity of Ensembles for Data Stream Classification [article]

Mohamed Souhayel Abassi
2019 arXiv   pre-print
The analysis provides a deeper understanding of the concept of diversity and its impact on online ensemble Learning in the presence of concept drift.  ...  Besides, ensembles of learning machines have been performed to learn in the presence of concept drift and adapt to it.  ...  Correlation can be calculated for a pair of Oracle outputs, their values are illustrated in table III as the probabilities for the respective pair of correct/incorrect outputs.  ... 
arXiv:1902.08466v1 fatcat:pa5eqgdwt5a5libyeidvgm6qdm

Effective Handling of Recurring Concept Drifts in Data Streams

Parneeta Dhaliwal, M. P. S. Bhatia
2017 Indian Journal of Science and Technology  
Objectives: In order to achieve better generalization accuracy while handling data with drifting concepts mainly recurrent drifts, we proposed an ensemble system called Recurring Dynamic Weighted Majority  ...  Findings: Experimental analysis using Stagger dataset shows that our system proves to be the best system for handling dataset containing abrupt as well as recurrent drifts, achieving the best prequential  ...  A learner is evaluated based on maximum of a pair of features with at least one of the features being irrelevant.  ... 
doi:10.17485/ijst/2017/v10i30/115498 fatcat:ezfikadpvbe3pbz5xz2olsz25q
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