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Statistical testing of segment homogeneity in classification of piecewise-regular objects

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Savchenko A., Belova N.

International Journal of Applied Mathematics and Computer Science

2015

tom 25

nr 4

915-925

The paper is focused on the problem of multi-class classification of composite (piecewise-regular) objects (e.g., speech signals, complex images, etc.). We propose a mathematical model of composite object representation as a sequence of independent segments. Each segment is represented as a random sample of independent identically distributed feature vectors. Based on this model and a statistical approach, we reduce the task to a problem of composite hypothesis testing of segment homogeneity. Several nearest-neighbor criteria are implemented, and for some of them the well-known special cases (e.g., the Kullback-Leibler minimum information discrimination principle, the probabilistic neural network) are highlighted. It is experimentally shown that the proposed approach improves the accuracy when compared with contemporary classifiers.

Ograniczanie wyników

1 International Journal of Applied Mathematics and Computer Science

1 Belova N.

1 Savchenko A.

1 2015

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Statistical testing of segment homogeneity in classification of piecewise-regular objects