Dynamical systems and nonlinear Kalman filtering applied in classification

In this paper we present an application for the dynamical systems and nonlinear filtering in classification. Described here method constitutes a new proposal for the classification of dynamical objects. This method does not require the expansion of dimensionality of the input vectors used in time series, compared to other methods which multiply inputs. The proposed classifier uses the dynamical systems as its core and nonlinear Kalman filtering as its learning algorithm. Presented herein results are the performance tests of proposed classifier applied in a real life problem - bankruptcy prediction, which is one of the fundamentals application of data mining in finance. The results achieved by the presented method were compared to other very popular classifiers and these experiments prove greater classification accuracy of the proposed method.