Application of Global Optimization Methods to Increase the Accuracy of Classification in the Data Mining Tasks

The article describes the solving of data mining task using neural-like structures of Successive Geometric Transformations Model (NLS SGTM). The main problems of this task are imbalanced dataset and different weigh of errors. Therefore, to take into account these features, the method of penalties and rewards was used, as well as a piecewise linear approach to classification. The supplement of the methods used by the final optimization procedure is proposed. The procedure of final optimization using simulated annealing.

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