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Paper details
Number 2 - June 2018
Volume 28 - 2018
Pattern layer reduction for a generalized regression neural network by using a self-organizing map
Serkan Kartal, Mustafa Oral, Buse Melis Ozyildirim
Abstract
In a general regression neural network (GRNN), the number of neurons in the pattern layer is proportional to the number of
training samples in the dataset. The use of a GRNN in applications that have relatively large datasets becomes troublesome
due to the architecture and speed required. The great number of neurons in the pattern layer requires a substantial increase
in memory usage and causes a substantial decrease in calculation speed. Therefore, there is a strong need for pattern layer
size reduction. In this study, a self-organizing map (SOM) structure is introduced as a pre-processor for the GRNN. First, an
SOM is generated for the training dataset. Second, each training record is labelled with the most similar map unit. Lastly,
when a new test record is applied to the network, the most similar map units are detected, and the training data that have
the same labels as the detected units are fed into the network instead of the entire training dataset. This scheme enables a
considerable reduction in the pattern layer size. The proposed hybrid model was evaluated by using fifteen benchmark test
functions and eight different UCI datasets. According to the simulation results, the proposed model significantly simplifies
the GRNN’s structure without any performance loss.
Keywords
generalized regression neural network, artificial neural network, self-organizing maps, nearest neighbour, reduced dataset