Standard
RDE with forgetting: an approximate solution for large values of k with an application to fault detection problems. / Bezerra, Clauder Gomez
; Costa, Bruno; Guedes, Luiz Affonso et al.
Statistical learning and data sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. ed. / Alexander Gammerman; Vladimir Vovk; Harris Papadopoulos. Springer, 2015. p. 169-178 (Lecture Notes in Computer Science; Vol. 9047).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Bezerra, CG
, Costa, B, Guedes, LA
& Angelov, P 2015,
RDE with forgetting: an approximate solution for large values of k with an application to fault detection problems. in A Gammerman, V Vovk & H Papadopoulos (eds),
Statistical learning and data sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. Lecture Notes in Computer Science, vol. 9047, Springer, pp. 169-178.
https://doi.org/10.1007/978-3-319-17091-6_12
APA
Bezerra, C. G.
, Costa, B., Guedes, L. A.
, & Angelov, P. (2015).
RDE with forgetting: an approximate solution for large values of k with an application to fault detection problems. In A. Gammerman, V. Vovk, & H. Papadopoulos (Eds.),
Statistical learning and data sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings (pp. 169-178). (Lecture Notes in Computer Science; Vol. 9047). Springer.
https://doi.org/10.1007/978-3-319-17091-6_12
Vancouver
Bezerra CG
, Costa B, Guedes LA
, Angelov P.
RDE with forgetting: an approximate solution for large values of k with an application to fault detection problems. In Gammerman A, Vovk V, Papadopoulos H, editors, Statistical learning and data sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. Springer. 2015. p. 169-178. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-17091-6_12
Author
Bezerra, Clauder Gomez
; Costa, Bruno ; Guedes, Luiz Affonso et al. /
RDE with forgetting : an approximate solution for large values of k with an application to fault detection problems. Statistical learning and data sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. editor / Alexander Gammerman ; Vladimir Vovk ; Harris Papadopoulos. Springer, 2015. pp. 169-178 (Lecture Notes in Computer Science).
Bibtex
@inproceedings{2e7ba84f005742d1bfb7d4ca13e67c74,
title = "RDE with forgetting: an approximate solution for large values of k with an application to fault detection problems",
abstract = "Recursive density estimation is a very powerful metric, based on a kernel function, used to detect outliers in a n-dimensional data set. Since it is calculated in a recursive manner, it becomes a very interesting solution for on-line and real-time applications. However, in its original formulation, the equation defined for density calculation is considerably conservative, which may not be suitable for applications that require fast response to dynamic changes in the process. For on-line applications, the value of k, which represents the index of the data sample, may increase indefinitely and, once that the mean update equation directly depends on the number of samples read so far, the influence of a new data sample may be nearly insignificant if the value of k is high. This characteristic creates, in practice, a stationary scenario that may not be adequate for fault detect applications, for example. In order to overcome this problem, we propose in this paper a new approach to RDE, holding its recursive characteristics. This new approach, called RDE with forgetting, introduces the concept of moving mean and forgetting factor, detailed in the next sections. The proposal is tested and validated on a very well known real data fault detection benchmark, however can be generalized to other problems.",
keywords = "Outlier detection, Novelty detection, Fault detection, Recursive density estimation",
author = "Bezerra, {Clauder Gomez} and Bruno Costa and Guedes, {Luiz Affonso} and Plamen Angelov",
year = "2015",
doi = "10.1007/978-3-319-17091-6_12",
language = "English",
isbn = "9783319170909",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "169--178",
editor = "Alexander Gammerman and Vladimir Vovk and Harris Papadopoulos",
booktitle = "Statistical learning and data sciences",
}
RIS
TY - GEN
T1 - RDE with forgetting
T2 - an approximate solution for large values of k with an application to fault detection problems
AU - Bezerra, Clauder Gomez
AU - Costa, Bruno
AU - Guedes, Luiz Affonso
AU - Angelov, Plamen
PY - 2015
Y1 - 2015
N2 - Recursive density estimation is a very powerful metric, based on a kernel function, used to detect outliers in a n-dimensional data set. Since it is calculated in a recursive manner, it becomes a very interesting solution for on-line and real-time applications. However, in its original formulation, the equation defined for density calculation is considerably conservative, which may not be suitable for applications that require fast response to dynamic changes in the process. For on-line applications, the value of k, which represents the index of the data sample, may increase indefinitely and, once that the mean update equation directly depends on the number of samples read so far, the influence of a new data sample may be nearly insignificant if the value of k is high. This characteristic creates, in practice, a stationary scenario that may not be adequate for fault detect applications, for example. In order to overcome this problem, we propose in this paper a new approach to RDE, holding its recursive characteristics. This new approach, called RDE with forgetting, introduces the concept of moving mean and forgetting factor, detailed in the next sections. The proposal is tested and validated on a very well known real data fault detection benchmark, however can be generalized to other problems.
AB - Recursive density estimation is a very powerful metric, based on a kernel function, used to detect outliers in a n-dimensional data set. Since it is calculated in a recursive manner, it becomes a very interesting solution for on-line and real-time applications. However, in its original formulation, the equation defined for density calculation is considerably conservative, which may not be suitable for applications that require fast response to dynamic changes in the process. For on-line applications, the value of k, which represents the index of the data sample, may increase indefinitely and, once that the mean update equation directly depends on the number of samples read so far, the influence of a new data sample may be nearly insignificant if the value of k is high. This characteristic creates, in practice, a stationary scenario that may not be adequate for fault detect applications, for example. In order to overcome this problem, we propose in this paper a new approach to RDE, holding its recursive characteristics. This new approach, called RDE with forgetting, introduces the concept of moving mean and forgetting factor, detailed in the next sections. The proposal is tested and validated on a very well known real data fault detection benchmark, however can be generalized to other problems.
KW - Outlier detection
KW - Novelty detection
KW - Fault detection
KW - Recursive density estimation
U2 - 10.1007/978-3-319-17091-6_12
DO - 10.1007/978-3-319-17091-6_12
M3 - Conference contribution/Paper
SN - 9783319170909
T3 - Lecture Notes in Computer Science
SP - 169
EP - 178
BT - Statistical learning and data sciences
A2 - Gammerman, Alexander
A2 - Vovk, Vladimir
A2 - Papadopoulos, Harris
PB - Springer
ER -