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RDE with forgetting: an approximate solution for large values of k with an application to fault detection problems

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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/ISSNConference contribution/Paperpeer-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 -