Lipschitz Classifiers Ensembles: Usage for Classification of Target Events in C-OTDR Monitoring Systems

This paper introduces an original method for
guaranteed estimation of the accuracy for an ensemble of Lipschitz
classifiers. The solution was obtained as a finite closed set of
alternative hypotheses, which contains an object of classification with
probability of not less than the specified value. Thus, the
classification is represented by a set of hypothetical classes. In this
case, the smaller the cardinality of the discrete set of hypothetical
classes is, the higher is the classification accuracy. Experiments have
shown that if cardinality of the classifiers ensemble is increased then
the cardinality of this set of hypothetical classes is reduced. The
problem of the guaranteed estimation of the accuracy for an ensemble
of Lipschitz classifiers is relevant in multichannel classification of
target events in C-OTDR monitoring systems. Results of suggested
approach practical usage to accuracy control in C-OTDR monitoring
systems are present.

• Andrey V. Timofeev

• Lipschitz classifiers
• confidence set
• C-OTDR monitoring
• classifiers accuracy
• classifiers ensemble.

[1] A.V. Timofeev, “The guaranteed estimation of the Lipschitz classifier
accuracy: Confidence set approach.” Journal Korean Stat. Soc. 41(1),
2012, pp.105-114. [2] L. Breiman, “Bagging predictors”, Mach. Learn. 24(2), 1996, pp. 123–
140.
[3] P. Rangel, F. Lozano, E. García, “Boosting of support vector machines
with application to editing”, Proceedings of the 4nd International
Conference of Machine Learning and Applications ICMLA’05, Springer
2005.
[4] C.H. You, K.A. Lee, H. Li, “A GMM supervector Kernel with the
Bhattacharyya distance for SVM based speaker recognition”, ICASSP,
IEEE International Conference on Acoustics, Speech and Signal
Processing, 2009, pp. 4221-4224.
[5] A.V. Timofeev, “The guaranteed detection of the seismoacoustic
emission source in the C- OTDR systems”, ICDSP 2014: International
Conference on Digital Signal Processing Conference Proceedings,
Barcelona, Vol.8 N. 10, 2014, pp. 979 – 982.
[6] U. Luxburg, O. Bousquet, “Distance-based classification with Lipschitz
functions”, Journal of Machine Learning Research, 5, 2004, pp. 669-
695.
[7] Kailath T. The Divergence and Bhattacharyya Distance Measures in
Signal Selection, IEEE Transactions on Communication Technology 15
(1), 1967, pp. 52-60.
[8] Timofeev A.V., Egorov D.V., Multichannel classification of target
signals by means of an SVM ensemble in C-OTDR systems for remote
monitoring of extended objects, MVML-2014 Conference Proceedings
V.1, Prague, 2014.