A meta-learning approach to methane concentration value prediction

Michał Kozielski

doi:10.1007/978-3-319-34099-9_56

A meta-learning approach to methane concentration value prediction

Michał Kozielski

Department of Computer Networks and Systems (RAU/RAU9)

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

4 Citations (Scopus)

Abstract

A meta-learning approach to stream data analysis is presented in this work. The analysis is based on prediction of methane concentration in a coal mine. The results of the analysis show that the chosen approach achieves relatively low error values. Additionally, the impact of a data window size on a learning speed and quality was verified. The analysis is performed on a stream of measurements that was generated on a basis of real values collected in a coal mine.

Original language	English
Title of host publication	Beyond Databases, Architectures and Structures
Subtitle of host publication	Advanced Technologies for Data Mining and Knowledge Discovery - 12th International Conference, BDAS 2016, Proceedings
Editors	Stanislaw Kozielski, Dariusz Mrozek, Pawel Kasprowski, Bozena Malysiak-Mrozek, Daniel Kostrzewa
Publisher	Springer Verlag
Pages	716-726
Number of pages	11
ISBN (Print)	9783319340982
DOIs	https://doi.org/10.1007/978-3-319-34099-9_56
Publication status	Published - 2016
Event	12th International Conference on Beyond Databases, Architectures and Structures, BDAS 2016 - Ustron, Poland Duration: 31 May 2016 → 3 Jun 2016

Publication series

Name	Communications in Computer and Information Science
Volume	613
ISSN (Print)	1865-0929

Conference

Conference	12th International Conference on Beyond Databases, Architectures and Structures, BDAS 2016
Country/Territory	Poland
City	Ustron
Period	31/05/16 → 3/06/16

Keywords

Algorithm selection
Meta-learning
Prediction
Stream data analysis

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-319-34099-9_56

Cite this

Kozielski, M. (2016). A meta-learning approach to methane concentration value prediction. In S. Kozielski, D. Mrozek, P. Kasprowski, B. Malysiak-Mrozek, & D. Kostrzewa (Eds.), Beyond Databases, Architectures and Structures: Advanced Technologies for Data Mining and Knowledge Discovery - 12th International Conference, BDAS 2016, Proceedings (pp. 716-726). (Communications in Computer and Information Science; Vol. 613). Springer Verlag. https://doi.org/10.1007/978-3-319-34099-9_56

Kozielski, Michał. / A meta-learning approach to methane concentration value prediction. Beyond Databases, Architectures and Structures: Advanced Technologies for Data Mining and Knowledge Discovery - 12th International Conference, BDAS 2016, Proceedings. editor / Stanislaw Kozielski ; Dariusz Mrozek ; Pawel Kasprowski ; Bozena Malysiak-Mrozek ; Daniel Kostrzewa. Springer Verlag, 2016. pp. 716-726 (Communications in Computer and Information Science).

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title = "A meta-learning approach to methane concentration value prediction",

abstract = "A meta-learning approach to stream data analysis is presented in this work. The analysis is based on prediction of methane concentration in a coal mine. The results of the analysis show that the chosen approach achieves relatively low error values. Additionally, the impact of a data window size on a learning speed and quality was verified. The analysis is performed on a stream of measurements that was generated on a basis of real values collected in a coal mine.",

keywords = "Algorithm selection, Meta-learning, Prediction, Stream data analysis",

author = "Micha{\l} Kozielski",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 12th International Conference on Beyond Databases, Architectures and Structures, BDAS 2016 ; Conference date: 31-05-2016 Through 03-06-2016",

year = "2016",

doi = "10.1007/978-3-319-34099-9\_56",

language = "English",

isbn = "9783319340982",

series = "Communications in Computer and Information Science",

publisher = "Springer Verlag",

pages = "716--726",

editor = "Stanislaw Kozielski and Dariusz Mrozek and Pawel Kasprowski and Bozena Malysiak-Mrozek and Daniel Kostrzewa",

booktitle = "Beyond Databases, Architectures and Structures",

address = "Germany",

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Kozielski, M 2016, A meta-learning approach to methane concentration value prediction. in S Kozielski, D Mrozek, P Kasprowski, B Malysiak-Mrozek & D Kostrzewa (eds), Beyond Databases, Architectures and Structures: Advanced Technologies for Data Mining and Knowledge Discovery - 12th International Conference, BDAS 2016, Proceedings. Communications in Computer and Information Science, vol. 613, Springer Verlag, pp. 716-726, 12th International Conference on Beyond Databases, Architectures and Structures, BDAS 2016, Ustron, Poland, 31/05/16. https://doi.org/10.1007/978-3-319-34099-9_56

A meta-learning approach to methane concentration value prediction. / Kozielski, Michał.
Beyond Databases, Architectures and Structures: Advanced Technologies for Data Mining and Knowledge Discovery - 12th International Conference, BDAS 2016, Proceedings. ed. / Stanislaw Kozielski; Dariusz Mrozek; Pawel Kasprowski; Bozena Malysiak-Mrozek; Daniel Kostrzewa. Springer Verlag, 2016. p. 716-726 (Communications in Computer and Information Science; Vol. 613).

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

TY - GEN

T1 - A meta-learning approach to methane concentration value prediction

AU - Kozielski, Michał

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - A meta-learning approach to stream data analysis is presented in this work. The analysis is based on prediction of methane concentration in a coal mine. The results of the analysis show that the chosen approach achieves relatively low error values. Additionally, the impact of a data window size on a learning speed and quality was verified. The analysis is performed on a stream of measurements that was generated on a basis of real values collected in a coal mine.

AB - A meta-learning approach to stream data analysis is presented in this work. The analysis is based on prediction of methane concentration in a coal mine. The results of the analysis show that the chosen approach achieves relatively low error values. Additionally, the impact of a data window size on a learning speed and quality was verified. The analysis is performed on a stream of measurements that was generated on a basis of real values collected in a coal mine.

KW - Algorithm selection

KW - Meta-learning

KW - Prediction

KW - Stream data analysis

UR - https://www.scopus.com/pages/publications/84964780388

U2 - 10.1007/978-3-319-34099-9_56

DO - 10.1007/978-3-319-34099-9_56

M3 - Conference contribution

AN - SCOPUS:84964780388

SN - 9783319340982

T3 - Communications in Computer and Information Science

SP - 716

EP - 726

BT - Beyond Databases, Architectures and Structures

A2 - Kozielski, Stanislaw

A2 - Mrozek, Dariusz

A2 - Kasprowski, Pawel

A2 - Malysiak-Mrozek, Bozena

A2 - Kostrzewa, Daniel

PB - Springer Verlag

T2 - 12th International Conference on Beyond Databases, Architectures and Structures, BDAS 2016

Y2 - 31 May 2016 through 3 June 2016

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Kozielski M. A meta-learning approach to methane concentration value prediction. In Kozielski S, Mrozek D, Kasprowski P, Malysiak-Mrozek B, Kostrzewa D, editors, Beyond Databases, Architectures and Structures: Advanced Technologies for Data Mining and Knowledge Discovery - 12th International Conference, BDAS 2016, Proceedings. Springer Verlag. 2016. p. 716-726. (Communications in Computer and Information Science). doi: 10.1007/978-3-319-34099-9_56

A meta-learning approach to methane concentration value prediction

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Keywords

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