  The importance of using fast and automatic methods to classify variable stars
for large amounts of data is undeniable. There have been many attempts to
classify variable stars by traditional algorithms like Random Forest. In recent
years, neural networks as classifiers have come to notice because of their
lower computational cost compared to traditional algorithms. This paper uses
the Hierarchical Classification technique, which contains two main steps of
predicting class and then subclass of stars. All the models in both steps have
same network structure and we test both Convolutional Neural Networks (CNN) and
Recurrent Neural Networks (RNN). Our pre-processing method uses light curves
and period of stars as input data. We consider most of the classes and
subclasses of variable stars in OGLE-IV database and show that using
Hierarchical Classification technique and designing appropriate preprocessing
can increase accuracy of predicting smaller classes, ACep and T2Cep. We obtain
an accuracy of 98% for class classification and 93% for subclasses
classification.
