Genelleştirilmiş regresyon yapay sinir ağı örüntü katman büyüklüğünü azaltmak için kümeleme tabanlı bir yaklaşım

Year 2018, Volume: 24 Issue: 5, 857 - 863, 12.10.2018

Mustafa Oral Serkan Kartal Buse Melis Özyıldırım

Abstract

Genelleştirilmiş Regresyon Yapay Sinir Ağı
(GRYSA) radyal tabanlı çalışan ve genellikle tahminleyici olarak kullanılan
denetimli-öğrenimli bir yapay sinir ağı (YSA) modelidir. Kolay
modellenebilmesinin yanında hızlı ve tutarlı sonuçlar üretmesi bu algoritmanın
güçlü yanlarını oluşturmaktadır. Ancak GRYSA tahmin mekanizmasında, eğitim veri
setindeki her örnek veri için örüntü katmanında bir adet nöron tutulmaktadır.
Bu nedenle, eğitim veri setinin çok büyük olduğu çalışmalarda örüntü katman
yapısı örnek verilerinin sayısıyla aynı oranda büyümekte, yapılan işlem sayısı
ve bellek gereksinimi artmaktadır. Bu çalışmada, GRYSA algoritmasının işlem
sayısını azaltmaya yönelik olarak literatürde daha önce de denenmiş olan
k-ortalama kümeleme algoritması ön-işlemci olarak kullanılmış, literatürdeki
çalışmalardan farklı olarak, bu çalışmaların performansını negatif anlamda
etkileyen kümeler arasına düşen test verileri bulunarak aykırı veri oluşmasının
önüne geçilmiştir. Böylece, örüntü katmanındaki bellek ihtiyacı ve işlem sayısı
azaltılırken, kümeleme algoritmasının eklenmesi ile performansta ortaya çıkan
negatif etki büyük oranda giderilmiş ve yaklaşık %90 daha az eğitim verisi ile
neredeyse aynı tahmin sonuçları elde edilmiştir.

Keywords

Genelleştirilmiş regresyon yapay sinir ağı, Tahminleyici yapay sinir ağı, Örüntü azaltma, Azaltılmış veri seti

A cluster based approach to reduce pattern layer size for generalized regression neural network

Abstract

Generalized
Regression Neural Network (GRNN), is a radial basis function based supervised
learning type Artificial Neural Network (ANN) which is commonly used for data
predictions. In addition to its easy modelling structure, being fast and
producing accurate results are the other strong features of it.  On the other hand, GRNN employs a neuron in
pattern layer for each data sample in training data set.  Therefore, for huge data sets pattern layer
size increases proportional to the number of samples in training data set,
memory requirement and computational time also increase excessively. In this
study, in order to reduce space and time complexity of GRNN, k-means clustering
algorithm which had been used as pre-processor in the literature is utilized
and outlier data emergence which affects the performances of previous studies
negatively, is prevented by identifying test data located between clusters.
Hence, while memory requirement in pattern layer and number of calculations are
reduced, negative effect on the performance emerged by the use of clustering
algorithm is significantly removed and almost the same prediction performances
to that of standard GRNN are achieved by using 90% less training samples.

Keywords

Generalized regression neural network, Prediction neural network, Pattern reduction, Reduced dataset

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