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Incremental anomaly identification by adapted SVM method

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Incremental anomaly identification by adapted SVM method. / Suvorov, Michail; Ivliev, Sergey; Markarian, Garik; Kolev, Denis; Zvikhachevskiy, Dmitry; Angelov, Plamen.

International Joint Conference on Neural Networks, IJCNN-2013, Dallas, TX, USA, 3-9 August, 2013. Piscataway, N.J. : IEEE, 2013. p. 1-8.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Suvorov, M, Ivliev, S, Markarian, G, Kolev, D, Zvikhachevskiy, D & Angelov, P 2013, Incremental anomaly identification by adapted SVM method. in International Joint Conference on Neural Networks, IJCNN-2013, Dallas, TX, USA, 3-9 August, 2013. IEEE, Piscataway, N.J., pp. 1-8. https://doi.org/10.1109/IJCNN.2013.6707031

APA

Suvorov, M., Ivliev, S., Markarian, G., Kolev, D., Zvikhachevskiy, D., & Angelov, P. (2013). Incremental anomaly identification by adapted SVM method. In International Joint Conference on Neural Networks, IJCNN-2013, Dallas, TX, USA, 3-9 August, 2013 (pp. 1-8). IEEE. https://doi.org/10.1109/IJCNN.2013.6707031

Vancouver

Suvorov M, Ivliev S, Markarian G, Kolev D, Zvikhachevskiy D, Angelov P. Incremental anomaly identification by adapted SVM method. In International Joint Conference on Neural Networks, IJCNN-2013, Dallas, TX, USA, 3-9 August, 2013. Piscataway, N.J.: IEEE. 2013. p. 1-8 https://doi.org/10.1109/IJCNN.2013.6707031

Author

Suvorov, Michail ; Ivliev, Sergey ; Markarian, Garik ; Kolev, Denis ; Zvikhachevskiy, Dmitry ; Angelov, Plamen. / Incremental anomaly identification by adapted SVM method. International Joint Conference on Neural Networks, IJCNN-2013, Dallas, TX, USA, 3-9 August, 2013. Piscataway, N.J. : IEEE, 2013. pp. 1-8

Bibtex

@inproceedings{ed8f301e2111430fbe71bb9486bdc804,
title = "Incremental anomaly identification by adapted SVM method",
abstract = "In our work we used the capability of one-class support vector machine (SVM) method to develop a novel one-class classification approach. Algorithm is designed and tested within the project SVETLANA aimed for fault detection in complex technological systems, such as aircraft. The main objective of this project was to create an algorithm responsible for collecting and analyzing the data since the launch of an aircraft engine. Data can be transferred from a variety of sensors that are responsible for the speed, oxygen level etc. In order to apply real time (in flight) application a recursive learning algorithm is proposed. The proposed method analyzes both “positive”/”normal” and “negative”/ “abnormal” examples The overall model structure is the same as an outlier-detection approach. The most important benefits of the new algorithm based on our algorithm are verified in comparison with several classifiers, including the traditional one-class SVM. This algorithm has been tested on real flight data from the USA, Western European as well as Russia. The test results are presented in the final part of the article.",
author = "Michail Suvorov and Sergey Ivliev and Garik Markarian and Denis Kolev and Dmitry Zvikhachevskiy and Plamen Angelov",
year = "2013",
month = aug,
doi = "10.1109/IJCNN.2013.6707031",
language = "English",
isbn = "9781467361286",
pages = "1--8",
booktitle = "International Joint Conference on Neural Networks, IJCNN-2013, Dallas, TX, USA, 3-9 August, 2013",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Incremental anomaly identification by adapted SVM method

AU - Suvorov, Michail

AU - Ivliev, Sergey

AU - Markarian, Garik

AU - Kolev, Denis

AU - Zvikhachevskiy, Dmitry

AU - Angelov, Plamen

PY - 2013/8

Y1 - 2013/8

N2 - In our work we used the capability of one-class support vector machine (SVM) method to develop a novel one-class classification approach. Algorithm is designed and tested within the project SVETLANA aimed for fault detection in complex technological systems, such as aircraft. The main objective of this project was to create an algorithm responsible for collecting and analyzing the data since the launch of an aircraft engine. Data can be transferred from a variety of sensors that are responsible for the speed, oxygen level etc. In order to apply real time (in flight) application a recursive learning algorithm is proposed. The proposed method analyzes both “positive”/”normal” and “negative”/ “abnormal” examples The overall model structure is the same as an outlier-detection approach. The most important benefits of the new algorithm based on our algorithm are verified in comparison with several classifiers, including the traditional one-class SVM. This algorithm has been tested on real flight data from the USA, Western European as well as Russia. The test results are presented in the final part of the article.

AB - In our work we used the capability of one-class support vector machine (SVM) method to develop a novel one-class classification approach. Algorithm is designed and tested within the project SVETLANA aimed for fault detection in complex technological systems, such as aircraft. The main objective of this project was to create an algorithm responsible for collecting and analyzing the data since the launch of an aircraft engine. Data can be transferred from a variety of sensors that are responsible for the speed, oxygen level etc. In order to apply real time (in flight) application a recursive learning algorithm is proposed. The proposed method analyzes both “positive”/”normal” and “negative”/ “abnormal” examples The overall model structure is the same as an outlier-detection approach. The most important benefits of the new algorithm based on our algorithm are verified in comparison with several classifiers, including the traditional one-class SVM. This algorithm has been tested on real flight data from the USA, Western European as well as Russia. The test results are presented in the final part of the article.

U2 - 10.1109/IJCNN.2013.6707031

DO - 10.1109/IJCNN.2013.6707031

M3 - Conference contribution/Paper

SN - 9781467361286

SP - 1

EP - 8

BT - International Joint Conference on Neural Networks, IJCNN-2013, Dallas, TX, USA, 3-9 August, 2013

PB - IEEE

CY - Piscataway, N.J.

ER -