Reservoir computing Echo State Network classifier training

Abstract

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 ESN binary classifiers are researched. We show that the reason of low precision of classification is using of unbalanced training dataset. Then the method to solve the problem is proposed. We propose to use stochastic perforation balancing algorithm on training data set and method of data temporalization. The resulting errors matrixes are pretty good. The proposed method is illustrated by the usage on synthetic data set. The features of ESN classifier are demonstrated in the case of anomaly events detection such as based on transaction attributes fraud detection.