Lipschitz Classifiers Ensembles: Usage for Classification of Target Events in C-OTDR Monitoring Systems
Authors: Andrey V. Timofeev
Abstract:
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.
Keywords: Lipschitz classifiers, confidence set, C-OTDR monitoring, classifiers accuracy, classifiers ensemble.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1100474
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[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.