Adaptive random forests for data stream regression

Heitor Murilo Gomes; Jean Paul Barddal; Luis Eduardo Boiko; Albert Bifet

Adaptive random forests for data stream regression

Heitor Murilo Gomes
, Jean Paul Barddal
, Luis Eduardo Boiko
, Albert Bifet

Université Paris-Saclay
Pontifícia Universidade Católica do Paraná

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

55 Citations (Scopus)

Résumé

Data stream mining is a hot topic in the machine learning community that tackles the problem of learning and updating predictive models as new data becomes available over time. Even though several new methods are proposed every year, most focus on the classification task and overlook the regression task. In this paper, we propose an adaptation to the Adaptive Random Forest so that it can handle regression tasks, namely ARF-Reg. ARF-Reg is empirically evaluated and compared to the state-of-the-art data stream regression algorithms, thus highlighting its applicability in different data stream scenarios.

langue originale	Anglais
titre	ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Editeur	i6doc.com publication
Pages	267-272
Nombre de pages	6
ISBN (Electronique)	9782875870476
état	Publié - 1 janv. 2018
Modification externe	Oui
Evénement	26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018 - Bruges, Belgique Durée: 25 avr. 2018 → 27 avr. 2018

Série de publications

Nom	ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Une conférence

Une conférence	26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018
Pays/Territoire	Belgique
La ville	Bruges
période	25/04/18 → 27/04/18

Contient cette citation

Gomes, H. M., Barddal, J. P., Boiko, L. E., & Bifet, A. (2018). Adaptive random forests for data stream regression. Dans ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (p. 267-272). (ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning). i6doc.com publication.

Gomes, Heitor Murilo ; Barddal, Jean Paul ; Boiko, Luis Eduardo et al. / Adaptive random forests for data stream regression. ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2018. p. 267-272 (ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

@inproceedings{cd3232c4f23d4aeca97f88db51209786,

title = "Adaptive random forests for data stream regression",

abstract = "Data stream mining is a hot topic in the machine learning community that tackles the problem of learning and updating predictive models as new data becomes available over time. Even though several new methods are proposed every year, most focus on the classification task and overlook the regression task. In this paper, we propose an adaptation to the Adaptive Random Forest so that it can handle regression tasks, namely ARF-Reg. ARF-Reg is empirically evaluated and compared to the state-of-the-art data stream regression algorithms, thus highlighting its applicability in different data stream scenarios.",

author = "Gomes, \{Heitor Murilo\} and Barddal, \{Jean Paul\} and Boiko, \{Luis Eduardo\} and Albert Bifet",

note = "Publisher Copyright: {\textcopyright} ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.; 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018 ; Conference date: 25-04-2018 Through 27-04-2018",

year = "2018",

month = jan,

day = "1",

language = "English",

series = "ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

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booktitle = "ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

}

Gomes, HM, Barddal, JP, Boiko, LE & Bifet, A 2018, Adaptive random forests for data stream regression. Dans ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, i6doc.com publication, p. 267-272, 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018, Bruges, Belgique, 25/04/18.

Adaptive random forests for data stream regression. / Gomes, Heitor Murilo; Barddal, Jean Paul; Boiko, Luis Eduardo et al.
ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2018. p. 267-272 (ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Adaptive random forests for data stream regression

AU - Gomes, Heitor Murilo

AU - Barddal, Jean Paul

AU - Boiko, Luis Eduardo

AU - Bifet, Albert

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Data stream mining is a hot topic in the machine learning community that tackles the problem of learning and updating predictive models as new data becomes available over time. Even though several new methods are proposed every year, most focus on the classification task and overlook the regression task. In this paper, we propose an adaptation to the Adaptive Random Forest so that it can handle regression tasks, namely ARF-Reg. ARF-Reg is empirically evaluated and compared to the state-of-the-art data stream regression algorithms, thus highlighting its applicability in different data stream scenarios.

AB - Data stream mining is a hot topic in the machine learning community that tackles the problem of learning and updating predictive models as new data becomes available over time. Even though several new methods are proposed every year, most focus on the classification task and overlook the regression task. In this paper, we propose an adaptation to the Adaptive Random Forest so that it can handle regression tasks, namely ARF-Reg. ARF-Reg is empirically evaluated and compared to the state-of-the-art data stream regression algorithms, thus highlighting its applicability in different data stream scenarios.

M3 - Conference contribution

AN - SCOPUS:85069492174

T3 - ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

SP - 267

EP - 272

BT - ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

PB - i6doc.com publication

T2 - 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018

Y2 - 25 April 2018 through 27 April 2018

ER -

Gomes HM, Barddal JP, Boiko LE, Bifet A. Adaptive random forests for data stream regression. Dans ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication. 2018. p. 267-272. (ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Adaptive random forests for data stream regression

Résumé

Série de publications

Une conférence

Empreinte digitale

Contient cette citation