Standard
OSA: one-class recursive SVM algorithm with negative samples for fault detection. / Suvorov, Michail; Ivliev, Sergey
; Markarian, Garik et al.
Artificial neural networks and machine learning – ICANN 2013: 23rd International Conference on Artificial Neural Networks Sofia, Bulgaria, September 10-13, 2013. Proceedings. ed. / Valeri Mladenov; Petia Koprinkova-Hristova; Günther Palm; Alessandro E. P. Villa; Bruno Appollini; Nikola Kasabov. Berlin: Springer Verlag, 2013. p. 194-207 (Lecture Notes in Computer Science; Vol. 8131).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Suvorov, M, Ivliev, S
, Markarian, G, Kolev, D, Zvikhachevskiy, D
& Angelov, P 2013,
OSA: one-class recursive SVM algorithm with negative samples for fault detection. in V Mladenov, P Koprinkova-Hristova, G Palm, AEP Villa, B Appollini & N Kasabov (eds),
Artificial neural networks and machine learning – ICANN 2013: 23rd International Conference on Artificial Neural Networks Sofia, Bulgaria, September 10-13, 2013. Proceedings. Lecture Notes in Computer Science, vol. 8131, Springer Verlag, Berlin, pp. 194-207.
https://doi.org/10.1007/978-3-642-40728-4_25APA
Suvorov, M., Ivliev, S.
, Markarian, G., Kolev, D., Zvikhachevskiy, D.
, & Angelov, P. (2013).
OSA: one-class recursive SVM algorithm with negative samples for fault detection. In V. Mladenov, P. Koprinkova-Hristova, G. Palm, A. E. P. Villa, B. Appollini, & N. Kasabov (Eds.),
Artificial neural networks and machine learning – ICANN 2013: 23rd International Conference on Artificial Neural Networks Sofia, Bulgaria, September 10-13, 2013. Proceedings (pp. 194-207). (Lecture Notes in Computer Science; Vol. 8131). Springer Verlag.
https://doi.org/10.1007/978-3-642-40728-4_25Vancouver
Suvorov M, Ivliev S
, Markarian G, Kolev D, Zvikhachevskiy D
, Angelov P.
OSA: one-class recursive SVM algorithm with negative samples for fault detection. In Mladenov V, Koprinkova-Hristova P, Palm G, Villa AEP, Appollini B, Kasabov N, editors, Artificial neural networks and machine learning – ICANN 2013: 23rd International Conference on Artificial Neural Networks Sofia, Bulgaria, September 10-13, 2013. Proceedings. Berlin: Springer Verlag. 2013. p. 194-207. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-40728-4_25
Author
Suvorov, Michail ; Ivliev, Sergey
; Markarian, Garik et al. /
OSA : one-class recursive SVM algorithm with negative samples for fault detection. Artificial neural networks and machine learning – ICANN 2013: 23rd International Conference on Artificial Neural Networks Sofia, Bulgaria, September 10-13, 2013. Proceedings. editor / Valeri Mladenov ; Petia Koprinkova-Hristova ; Günther Palm ; Alessandro E. P. Villa ; Bruno Appollini ; Nikola Kasabov. Berlin : Springer Verlag, 2013. pp. 194-207 (Lecture Notes in Computer Science).
Bibtex
@inproceedings{7552be32dba048e099cc4c8ec1a25798,
title = "OSA: one-class recursive SVM algorithm with negative samples for fault detection",
abstract = "In this paper a novel one-class classification approach (called OSA) is proposed. The algorithm is particularly suitable for fault detection in complex technological systems, such as aircraft. This study is based on the capability of one-class support vector machine (SVM) method to classify correctly the observation and measurement data, obtained during the exploitation of the system such as airborne aircraft into a single class of {\textquoteleft}normal{\textquoteright} behavior and, respectively, leave data that is not assigned to this class as suspected anomalies. In order to ensure real time (in flight) application a recursive learning procedure of the method is proposed. The proposed method takes into account both “positive”/“normal” and “negative”/“abnormal” examples of the base class, keeping the overall model structure as an outlier-detection approach. This approach is generic for any fault detection problem (for example in areas such as process control, computer networks, analysis of data from interrogations, etc.). The advantages of the new algorithm based on OSA are verified by comparison with several classifiers, including the traditional one-class SVM. The proposed approach is tested for fault detection problem using real flight data from a large number of aircraft of different make (USA, Western European as well as Russian).",
keywords = "Flight Data Analysis, Fault Detection and Identification, One-class SVM",
author = "Michail Suvorov and Sergey Ivliev and Garik Markarian and Denis Kolev and Dmitry Zvikhachevskiy and Plamen Angelov",
year = "2013",
doi = "10.1007/978-3-642-40728-4_25",
language = "English",
isbn = "9783642407277",
series = "Lecture Notes in Computer Science",
publisher = "Springer Verlag",
pages = "194--207",
editor = "Valeri Mladenov and Petia Koprinkova-Hristova and G{\"u}nther Palm and Villa, {Alessandro E. P.} and Bruno Appollini and Kasabov, {Nikola }",
booktitle = "Artificial neural networks and machine learning – ICANN 2013",
}
RIS
TY - GEN
T1 - OSA
T2 - one-class recursive SVM algorithm with negative samples for fault detection
AU - Suvorov, Michail
AU - Ivliev, Sergey
AU - Markarian, Garik
AU - Kolev, Denis
AU - Zvikhachevskiy, Dmitry
AU - Angelov, Plamen
PY - 2013
Y1 - 2013
N2 - In this paper a novel one-class classification approach (called OSA) is proposed. The algorithm is particularly suitable for fault detection in complex technological systems, such as aircraft. This study is based on the capability of one-class support vector machine (SVM) method to classify correctly the observation and measurement data, obtained during the exploitation of the system such as airborne aircraft into a single class of ‘normal’ behavior and, respectively, leave data that is not assigned to this class as suspected anomalies. In order to ensure real time (in flight) application a recursive learning procedure of the method is proposed. The proposed method takes into account both “positive”/“normal” and “negative”/“abnormal” examples of the base class, keeping the overall model structure as an outlier-detection approach. This approach is generic for any fault detection problem (for example in areas such as process control, computer networks, analysis of data from interrogations, etc.). The advantages of the new algorithm based on OSA are verified by comparison with several classifiers, including the traditional one-class SVM. The proposed approach is tested for fault detection problem using real flight data from a large number of aircraft of different make (USA, Western European as well as Russian).
AB - In this paper a novel one-class classification approach (called OSA) is proposed. The algorithm is particularly suitable for fault detection in complex technological systems, such as aircraft. This study is based on the capability of one-class support vector machine (SVM) method to classify correctly the observation and measurement data, obtained during the exploitation of the system such as airborne aircraft into a single class of ‘normal’ behavior and, respectively, leave data that is not assigned to this class as suspected anomalies. In order to ensure real time (in flight) application a recursive learning procedure of the method is proposed. The proposed method takes into account both “positive”/“normal” and “negative”/“abnormal” examples of the base class, keeping the overall model structure as an outlier-detection approach. This approach is generic for any fault detection problem (for example in areas such as process control, computer networks, analysis of data from interrogations, etc.). The advantages of the new algorithm based on OSA are verified by comparison with several classifiers, including the traditional one-class SVM. The proposed approach is tested for fault detection problem using real flight data from a large number of aircraft of different make (USA, Western European as well as Russian).
KW - Flight Data Analysis
KW - Fault Detection and Identification
KW - One-class SVM
U2 - 10.1007/978-3-642-40728-4_25
DO - 10.1007/978-3-642-40728-4_25
M3 - Conference contribution/Paper
SN - 9783642407277
T3 - Lecture Notes in Computer Science
SP - 194
EP - 207
BT - Artificial neural networks and machine learning – ICANN 2013
A2 - Mladenov, Valeri
A2 - Koprinkova-Hristova, Petia
A2 - Palm, Günther
A2 - Villa, Alessandro E. P.
A2 - Appollini, Bruno
A2 - Kasabov, Nikola
PB - Springer Verlag
CY - Berlin
ER -
