Bilişim Teknolojileri Dergisi

Öz

Many classification methods in the literature are used to achieve the
highest classification accuracy. The restricted Boltzmann machine is an
artificial neural network with unsupervised learning, however it is gained
importance as a learning component to extract attributes. In our study, we
compared the performances of five different pipelines builded with the
Bernoulli-type restricted Boltzmann machine and different classifiers.
Experiments have been carried out on these pipelines by using logistic
regression, decision tree, Gaussian naive Bayes, Ada Boost, and random forest
classifier, respectively. The classification resultant changes were observed
through the use of pipelines compared to the stand alone classification results
obtained by these classifiers. Experimental results were obtained with the use
of MNIST handwriting digit recognition data set. In these experiments, two
different orders of hyper parameters of the restricted Boltzmann machine were
used. According to these results, it was seen that the classification accuracy
of the stand alone classifier became better through the use of pipelines in the
experiments. The highest performance was achieved with a classification success
rate of 97.19%. Models using the proposed pipeline design have improved the average
performance of related invidividual classifiers from at least 1% to at most 33%.
Discussions are also included.