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Adaptive random forests for evolving data stream classification

Heitor M. Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabrício Enembreck, Bernhard Pfharinger, Geoff Holmes, Talel Abdessalem
2017 Machine Learning  
In this work, we present the adaptive random forest (ARF) algorithm for  ...  However, in the challenging context of evolving data streams, there is no random forests algorithm that can be considered state-of-the-art in comparison to bagging and boosting based algorithms.  ...  financially supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) through the Programa de Suporte à Pós-Graduação de Instituições de Ensino Particulares (PROSUP) program for  ... 
doi:10.1007/s10994-017-5642-8 fatcat:jn7gytjujfeuhjilhtpop6gsci

Correction to: Predictive intelligence to the edge: impact on edge analytics

Natascha Harth, Christos Anagnostopoulos, Dimitrios Pezaros
2017 Evolving Systems  
Based on this on-line decision making, we eliminate data transfer at the edge of the network, thus saving network resources by exploiting the evolving nature of the captured contextual data.  ...  This enables a huge amount of rich contextual data to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud.  ...  data streams.  ... 
doi:10.1007/s12530-017-9210-z fatcat:m4m57npzpjhljnvznipuiukhhe

A Numerical Transform of Random Forest Regressors corrects Systematically-Biased Predictions [article]

Shipra Malhotra, John Karanicolas
2020 arXiv   pre-print
Here we demonstrate the basis for this problem, and we use the training data to define a numerical transformation that fully corrects it.  ...  Over the past decade, random forest models have become widely used as a robust method for high-dimensional data regression tasks.  ...  Acknowledgements We thank Yusuf Adeshina for helpful discussions.  ... 
arXiv:2003.07445v1 fatcat:we6un4c7dbgp7ds63drxsozbqm

Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them [chapter]

Albert Bifet, Jesse Read, Indrė Žliobaitė, Bernhard Pfahringer, Geoff Holmes
2013 Lecture Notes in Computer Science  
Data stream classification plays an important role in modern data analysis, where data arrives in a stream and needs to be mined in real time.  ...  In response to the temporal dependence issue we propose a generic wrapper for data stream classifiers, which incorporates the temporal component into the attribute space.  ...  Introduction Data streams refer to a type of data, that is generated in real-time, arrives continuously as a stream and may be evolving over time.  ... 
doi:10.1007/978-3-642-40988-2_30 fatcat:irwwo5tsfffujo43sydetioo7y

Ensemble Classification for Drifting Concept

E. Padmalatha, C. R. K. Reddy, B. Padmaja Rani
2013 International Journal of Computer Applications  
Traditional data mining classifiers are used for mining the static data, in which incremental learning assumed data streams come under stationary distribution where data concepts remain unchanged.  ...  Their modularity provides natural path of absorbing changes by modifying ensemble member.The proposed approach uses ensemble classifiers to improve the accuracy of the classification in data streams .The  ...  The stream classifier must evolve to effectively indicate current class distribution in case of evolving data streams [13] . There are two widely used classification approaches: train the classifiers  ... 
doi:10.5120/13908-1857 fatcat:gtlu7kkstnhtjpri3jj4iwiqhm

Calculating feature importance in data streams with concept drift using Online Random Forest

Andrew Phelps Cassidy, Frank A. Deviney
2014 2014 IEEE International Conference on Big Data (Big Data)  
Online Random Forest (ORF) is one such approach to streaming classification problems.  ...  We adapted the feature importance metrics of Mean Decrease in Accuracy (MDA) and Mean Decrease in Gini Impurity (MDG), both originally designed for offline Random Forest, to Online Random Forest so that  ...  We begin with a brief discussion of the original Random Forest algorithm, and then discuss adapting it to the streaming realm.  ... 
doi:10.1109/bigdata.2014.7004352 dblp:conf/bigdataconf/CassidyD14 fatcat:luvfadag6jacdnjuos3kjfo4ii

Ensemble based on Accuracy and Diversity Weighting for Evolving Data Streams

Yange Sun, Han Shao, Bencai Zhang
2022 ˜The œinternational Arab journal of information technology  
ADE mainly uses the following three steps to construct a concept-drift oriented ensemble: for the current data window, 1) a new base classifier is constructed based on the current concept when drift detect  ...  Experimental results show that the proposed method can effectively adapt to different types of drifts.  ...  Adaptive Random Forest (ARF) [11] is an improved random forest algorithm based on diversity. Recently, Sun et al.  ... 
doi:10.34028/iajit/19/1/11 fatcat:dldopmxl5zbs5dz6nucpqfspua

Terrestrial reproduction as an adaptation to steep terrain in African toads

H. Christoph Liedtke, Hendrik Müller, Julian Hafner, Johannes Penner, David J. Gower, Tomáš Mazuch, Mark-Oliver Rödel, Simon P. Loader
2017 Proceedings of the Royal Society of London. Biological Sciences  
as adaptations to particular abiotic habitat parameters.  ...  Evolutionary transitions to terrestrial modes of reproduction occurred synchronously with or after transitions in habitat, and we, therefore, interpret terrestrial breeding as an adaptation to these abiotic  ...  Duplicate records across data sources and duplicate records per species falling into the same grid cell for climatic layers were removed.  ... 
doi:10.1098/rspb.2016.2598 pmid:28356450 pmcid:PMC5378084 fatcat:p6vaboy5jretfhmne6pcjsl2ui

Image Classification to Support Emergency Situation Awareness

Ryan Lagerstrom, Yulia Arzhaeva, Piotr Szul, Oliver Obst, Robert Power, Bella Robinson, Tomasz Bednarz
2016 Frontiers in Robotics and AI  
Emergency service operators are interested in having images relevant to such fires reported as extra information to help manage evolving emergencies.  ...  Specifically, we investigate image classification in the context of a bush fire emergency in the Australian state of NSW, where images associated with Tweets during the emergency were used to train and  ...  We opted for using a random forest model due to its good trade-off between accuracy and model interpretability.  ... 
doi:10.3389/frobt.2016.00054 fatcat:cfymrhpxpvaszdjug7i3cmb7ry

A classifier using online bagging ensemble method for big data stream learning

Yanxia Lv, Sancheng Peng, Ying Yuan, Cong Wang, Pengfei Yin, Jiemin Liu, Cuirong Wang
2019 Tsinghua Science and Technology  
Results show that the proposed algorithm can obtain better accuracy and more feasible usage of resources for the classification of big data stream.  ...  In this paper, we present an efficient classifier using the online bagging ensemble method for big data stream learning.  ...  adapt to a constantly evolving stream with data arriving at high speeds.  ... 
doi:10.26599/tst.2018.9010119 fatcat:rvonyk2bjnhzxcm47bmwfu4imi

Metalearning and Algorithm Selection: progress, state of the art and introduction to the 2018 Special Issue

Pavel Brazdil, Christophe Giraud-Carrier
2017 Machine Learning  
Our main aim is to highlight how the papers selected for this special issue contribute to the field of metalearning.  ...  In the the first section, we give an overview of how the field of metalearning has evolved in the last 1-2 decades and mention how some of the papers in this special issue fit in.  ...  authors, to Machine Learning's Editor-in-Chief for allowing us to produce this Special Issue and for offering valuable comments throughout, and to the editorial and publishing staff at Springer for bringing  ... 
doi:10.1007/s10994-017-5692-y fatcat:kurxx4tm5veoxd4edjrraki2ou

Self-adaptive heterogeneous random forest

Mohamed Bader-El-Den
2014 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA)  
Random Forest RF is an ensemble learning approach that utilises a number of classifiers to contribute though voting to predicting the class label of any unlabelled instances.  ...  This population of forests is then evolved through a number of generations using genetic algorithms.  ...  DATA SETS USED random forest (RFin), we have conducted a series of experi- ments reporting the percentage of trees voted in the random forest, contributing to the correct classification.  ... 
doi:10.1109/aiccsa.2014.7073259 dblp:conf/aiccsa/Bader-El-Den14 fatcat:xlaxgtztpzaepjgxrwymvu3lwy

Spam, a Digital Pollution and Ways to Eradicate It

2019 International Journal of Engineering and Advanced Technology  
Spammers on Twitter seem to be more dangerous than the mail spammers as they exploit the limitation on the characters of Twitter for their own purposes.  ...  Spammers have also become creative in framing their content to cleverly escape the classifiers.  ...  ., Random Forest, SVM, J48) is applied to the new labelled feature space to construct a binary classification model to supplant the present classifier model.  ... 
doi:10.35940/ijeat.b4107.129219 fatcat:uze7gfg3wrgjdmetvpuhzhl7p4

Leveraging Bagging for Evolving Data Streams [chapter]

Albert Bifet, Geoff Holmes, Bernhard Pfahringer
2010 Lecture Notes in Computer Science  
Attempts have been made to reproduce these methods in the more challenging context of evolving data streams. In this paper, we propose a new variant of bagging, called leveraging bagging.  ...  Bagging, boosting and Random Forests are classical ensemble methods used to improve the performance of single classifiers.  ...  Hoeffding trees [14] are state-of-the-art in classification for data streams and they perform prediction by choosing the majority class at each leaf.  ... 
doi:10.1007/978-3-642-15880-3_15 fatcat:dbfsxm7ofbevdehnk6725kikxm

Network Sampling: From Static to Streaming Graphs [article]

Nesreen K. Ahmed and Jennifer Neville and Ramana Kompella
2012 arXiv   pre-print
that is appropriate for streaming domains.  ...  Since many real-world networks are massive in size, continuously evolving, and/or distributed in nature, the network structure is often sampled in order to facilitate study.  ...  load shedding [Tatbul et al. 2003 ], for mining concept drifting data streams Gao et al. 2007; Fan 2004b; Fan 2004a] , clustering evolving data streams [Guha et al. 2003; Aggarwal et al. 2003 ], active  ... 
arXiv:1211.3412v1 fatcat:4k3vrxwe65h3nisl323d27qeby
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