Neural Network Architecture Selection in Classification Problem

Neural network processing of high-dimensional data requires significant computing resources. An incorrect choice of neural network architecture or data transformation can lead to significant losses of time and resources in network retraining. This article proposes an algorithm for selecting the architecture of compressive layers for a deep belief network and data transformation based on the analysis of a multi-label confusion matrix. The introduced generalized skewness coefficient describes the uniformity of the distribution of objects between neural network classes and allows for a numerical evaluation of the effectiveness of the network’s compressive layer architecture and data transformation. Using an applied problem as an example, it is demonstrated that the proposed algorithm improves the quality of its solution by more than 10 % by selecting the most effective neural network architecture.

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