Reservoir computing Echo State Network classifier training
Reservoir Computing (RC) is taking attention of neural networks structures developers because of machine learning algorithms are simple at the high level of generalization of the models. The approaches are numerous. RC can be applied to different architectures including recurrent neural networks with irregular connections that are called Echo State Networks (ESN). However, the existence of successful examples of chaotic sequences predictions does not provide successful method of multiple attribute objects classification.
In this paper the binary ESN classifiers are researched. We show that the reason of low precision of classification is the existence of unbalanced classes. Then the method to solve the problem is proposed. It is possible to use randomizing algorithm of learning data set balancing and method of data temporalization. The resulting errors matrixes have pretty good numbers. The proposed method is illustrated by the usage on synthetic data set. The features of ESN classifier are demonstrated in the case of rare events detection such as transaction attributes fraud detection.