Abstract: The paper presents new results concerning selection of
optimal information fusion formula for ensembles of C-OTDR
channels. The goal of information fusion is to create an integral
classificator designed for effective classification of seismoacoustic
target events. The LPBoost (LP-β and LP-B variants), the Multiple
Kernel Learning, and Weighing of Inversely as Lipschitz Constants
(WILC) approaches were compared. The WILC is a brand new
approach to optimal fusion of Lipschitz Classifiers Ensembles.
Results of practical usage are presented.
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.