MODULAR CONNECTIONIST MODELLING AND CLASSIFICATION APPROACHES FOR LOCAL DIAGNOSIS IN TELECOMMUNICATION TRAFFIC MANAGEMENT

Abstract

In Neural Networks (NN) applications, it is common practice to train several different networks and select the best one on the basis of performance on a validation set. There is a major disadvantage of such an approach: the network with the best performance on the validation set might not be the best one on new test data. In fact, the generalization performance on the validation set has a random component that is due to the data noise. An alternative approach to this problem is to use a combination of multiple NN classifiers. It is well known that such combinations can produce better performance than the best single network used in isolation. Several studies in different fields of the pattern recognition have experimentally shown that an appropriate combination of NN classifiers allows to capture complex phenomena, and to make decisions even with a great deal of information.3,11,12 The combination of multiple classifiers is a general problem in the pattern recognition area. Several methods for combining the outputs of multiple classifiers have been proposed: ensemble methods,14 boosting approaches,9 stacking techniques,27 multi-expert systems,2,16 and multi-modular architectures.3,13,18 A very rich synthesis of different methods of combining classifiers can be found in Ref. 28. An analytical framework to quantify the improvements in classification results that is due to combining classifiers is provided in Ref. 26. In this paper some contributions in combining multiple NN classifiers are presented. We give a combinational approach based on multi-modular systems for local diagnosis problem in the telephone network. In addition, we propose a combinational method for discrimination task using a fusion. The system validation will be performed on a disturbance identification problem.