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
Programa de Pos-Graduacao em Informatica (PPGIa)
Pontifícia Universidade Católica do Paraná

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

55 Citations (Scopus)

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.

Original language	English
Title of host publication	ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publisher	i6doc.com publication
Pages	267-272
Number of pages	6
ISBN (Electronic)	9782875870476
Publication status	Published - 1 Jan 2018
Externally published	Yes
Event	26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018 - Bruges, Belgium Duration: 25 Apr 2018 → 27 Apr 2018

Publication series

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

Conference

Conference	26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018
Country/Territory	Belgium
City	Bruges
Period	25/04/18 → 27/04/18

Cite this

Gomes, H. M., Barddal, J. P., Boiko, L. E., & Bifet, A. (2018). Adaptive random forests for data stream regression. In ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 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. pp. 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",

publisher = "i6doc.com publication",

pages = "267--272",

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. in 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, pp. 267-272, 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018, Bruges, Belgium, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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. In 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

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