Title: ServeNet: A Deep Neural Network for Web Service Classification,
Abstract: Automated service classification plays a crucial role in service management
such as service discovery, selection, and composition. In recent years, machine
learning techniques have been used for service classification. However, they
can only predict around 10 to 20 service categories due to the quality of
feature engineering and the imbalance problem of service dataset. In this
paper, we present a deep neural network ServeNet with a novel dataset splitting
algorithm to deal with these issues. ServeNet can automatically abstract
low-level representation to high-level features, and then predict service
classification based on the service datasets produced by the proposed splitting
algorithm. To demonstrate the effectiveness of our approach, we conducted a
comprehensive experimental study on 10,000 real-world services in 50
categories. The result shows that ServeNet can achieve higher accuracy than
other machine learning methods.