Standard
Concept Drift Detection Using Autoencoders in Data Streams Processing. / Jaworski, Maciej; Rutkowski, Leszek
; Angelov, Plamen.
Artificial Intelligence and Soft Computing: 19th International Conference, ICAISC 2020, Zakopane, Poland, October 12-14, 2020, Proceedings, Part I. ed. / Leszek Rutkowski; Rafał Scherer; Marcin Korytkowski; Witold Pedrycz; Ryszard Tadeusiewicz; Jacek M. Zurada. Cham: Springer, 2020. p. 124-133 (Lecture Notes in Computer Science; Vol. 12415 ).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Jaworski, M, Rutkowski, L
& Angelov, P 2020,
Concept Drift Detection Using Autoencoders in Data Streams Processing. in L Rutkowski, R Scherer, M Korytkowski, W Pedrycz, R Tadeusiewicz & JM Zurada (eds),
Artificial Intelligence and Soft Computing: 19th International Conference, ICAISC 2020, Zakopane, Poland, October 12-14, 2020, Proceedings, Part I. Lecture Notes in Computer Science, vol. 12415 , Springer, Cham, pp. 124-133.
https://doi.org/10.1007/978-3-030-61401-0_12APA
Jaworski, M., Rutkowski, L.
, & Angelov, P. (2020).
Concept Drift Detection Using Autoencoders in Data Streams Processing. In L. Rutkowski, R. Scherer, M. Korytkowski, W. Pedrycz, R. Tadeusiewicz, & J. M. Zurada (Eds.),
Artificial Intelligence and Soft Computing: 19th International Conference, ICAISC 2020, Zakopane, Poland, October 12-14, 2020, Proceedings, Part I (pp. 124-133). (Lecture Notes in Computer Science; Vol. 12415 ). Springer.
https://doi.org/10.1007/978-3-030-61401-0_12Vancouver
Jaworski M, Rutkowski L
, Angelov P.
Concept Drift Detection Using Autoencoders in Data Streams Processing. In Rutkowski L, Scherer R, Korytkowski M, Pedrycz W, Tadeusiewicz R, Zurada JM, editors, Artificial Intelligence and Soft Computing: 19th International Conference, ICAISC 2020, Zakopane, Poland, October 12-14, 2020, Proceedings, Part I. Cham: Springer. 2020. p. 124-133. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-61401-0_12
Author
Jaworski, Maciej ; Rutkowski, Leszek
; Angelov, Plamen. /
Concept Drift Detection Using Autoencoders in Data Streams Processing. Artificial Intelligence and Soft Computing: 19th International Conference, ICAISC 2020, Zakopane, Poland, October 12-14, 2020, Proceedings, Part I. editor / Leszek Rutkowski ; Rafał Scherer ; Marcin Korytkowski ; Witold Pedrycz ; Ryszard Tadeusiewicz ; Jacek M. Zurada. Cham : Springer, 2020. pp. 124-133 (Lecture Notes in Computer Science).
Bibtex
@inproceedings{bfde2aa09b734083ae5a5ecd357827c9,
title = "Concept Drift Detection Using Autoencoders in Data Streams Processing",
abstract = "In this paper, the problem of concept drift detection in data stream mining algorithms is considered. The autoencoder is proposed to be applied as a drift detector. The autoencoders are neural networks that are learned how to reconstruct input data. As a side effect, they are able to learn compact nonlinear codes, which summarize the most important features of input data. We suspect that the properly learned autoencoder on one part of the data stream can then be used to monitor possible changes in the following stream parts. The changes are analyzed by monitoring variations of the autoencoder cost function. Two cost functions are applied in this paper: the cross-entropy and the reconstruction error. Preliminary experimental results show that the proposed autoencoder-based detector is able to handle different types of concept drift, e.g. the sudden or the gradual. {\textcopyright} 2020, Springer Nature Switzerland AG.",
keywords = "Autoencoder, Concept drift detection, Data stream mining, Artificial intelligence, Cost functions, Data mining, Input output programs, Learning systems, Soft computing, Auto encoders, Concept drifts, Data streams processing, Drift detectors, Important features, Nonlinear codes, Reconstruction error, Data streams",
author = "Maciej Jaworski and Leszek Rutkowski and Plamen Angelov",
note = "The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-61401-0_12",
year = "2020",
month = oct,
day = "7",
doi = "10.1007/978-3-030-61401-0_12",
language = "English",
isbn = "9783030614003 ",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "124--133",
editor = "Leszek Rutkowski and Rafa{\l} Scherer and Marcin Korytkowski and Witold Pedrycz and Ryszard Tadeusiewicz and Zurada, {Jacek M.}",
booktitle = "Artificial Intelligence and Soft Computing",
}
RIS
TY - GEN
T1 - Concept Drift Detection Using Autoencoders in Data Streams Processing
AU - Jaworski, Maciej
AU - Rutkowski, Leszek
AU - Angelov, Plamen
N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-61401-0_12
PY - 2020/10/7
Y1 - 2020/10/7
N2 - In this paper, the problem of concept drift detection in data stream mining algorithms is considered. The autoencoder is proposed to be applied as a drift detector. The autoencoders are neural networks that are learned how to reconstruct input data. As a side effect, they are able to learn compact nonlinear codes, which summarize the most important features of input data. We suspect that the properly learned autoencoder on one part of the data stream can then be used to monitor possible changes in the following stream parts. The changes are analyzed by monitoring variations of the autoencoder cost function. Two cost functions are applied in this paper: the cross-entropy and the reconstruction error. Preliminary experimental results show that the proposed autoencoder-based detector is able to handle different types of concept drift, e.g. the sudden or the gradual. © 2020, Springer Nature Switzerland AG.
AB - In this paper, the problem of concept drift detection in data stream mining algorithms is considered. The autoencoder is proposed to be applied as a drift detector. The autoencoders are neural networks that are learned how to reconstruct input data. As a side effect, they are able to learn compact nonlinear codes, which summarize the most important features of input data. We suspect that the properly learned autoencoder on one part of the data stream can then be used to monitor possible changes in the following stream parts. The changes are analyzed by monitoring variations of the autoencoder cost function. Two cost functions are applied in this paper: the cross-entropy and the reconstruction error. Preliminary experimental results show that the proposed autoencoder-based detector is able to handle different types of concept drift, e.g. the sudden or the gradual. © 2020, Springer Nature Switzerland AG.
KW - Autoencoder
KW - Concept drift detection
KW - Data stream mining
KW - Artificial intelligence
KW - Cost functions
KW - Data mining
KW - Input output programs
KW - Learning systems
KW - Soft computing
KW - Auto encoders
KW - Concept drifts
KW - Data streams processing
KW - Drift detectors
KW - Important features
KW - Nonlinear codes
KW - Reconstruction error
KW - Data streams
U2 - 10.1007/978-3-030-61401-0_12
DO - 10.1007/978-3-030-61401-0_12
M3 - Conference contribution/Paper
SN - 9783030614003
T3 - Lecture Notes in Computer Science
SP - 124
EP - 133
BT - Artificial Intelligence and Soft Computing
A2 - Rutkowski, Leszek
A2 - Scherer, Rafał
A2 - Korytkowski, Marcin
A2 - Pedrycz, Witold
A2 - Tadeusiewicz, Ryszard
A2 - Zurada, Jacek M.
PB - Springer
CY - Cham
ER -
