THE QUICK METHOD OF TRAINING SAMPLE SELECTION FOR NEURAL NETWORK DECISION MAKING MODEL BUILDING ON PRECEDENTS

Authors

  • S. A. Subbotin Zaporizhzhya National Technical University, Zaporizhzhya, Ukraine, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2015-1-6

Keywords:

sample, sampling, instance, neural network, individual prediction, training on precedents.

Abstract

The problem of training sample forming is solved to automate the construction of neural network models on precedents. The sampling
method is proposed. It automatically selects the training and test samples from the original sample without the need for downloading the entire original sample to the computer memory. It processes an initial sample for each one instance with hashing transformation to a onedimensional axis, forming cluster templates on the generalized axis, minimizing their number. This allows to increase the speed of sampling, to reduce the requirements to computing resources and to computer memory and to provide an acceptable level of accuracy of the synthesized models. The developed method does not require multiple passes through the sample, being limited by only three viewing. At the same time the method keeps in a random access memory only the current instance and the generated set of one-dimensional templates, which is minimized by volume. Unlike the methods based on random sampling and cluster analysis the proposed method automatically determines the size of the formed training and test samples without the need for human intervention. Software realizing proposed method is developed. On its basis the practical task of decision-making model building to predict the individual state of the patient with hypertension is resolved.

References

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Published

2015-01-20

How to Cite

Subbotin, S. A. (2015). THE QUICK METHOD OF TRAINING SAMPLE SELECTION FOR NEURAL NETWORK DECISION MAKING MODEL BUILDING ON PRECEDENTS. Radio Electronics, Computer Science, Control, (1). https://doi.org/10.15588/1607-3274-2015-1-6

Issue

No. 1 (2015): Radio Electronics, Computer Science, Control

Section

Neuroinformatics and intelligent systems

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Copyright (c) 2015 S. A. Subbotin

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