Transfer Learning for Web Services Classification

Abstract

Web service classification is one of the common approaches to discover and reuse services. Machine learning methods are widely used for web service classification. However, due to the limited high-quality services in the public dataset, the state-of-the-art deep learning methods can not achieve high accuracy. In this paper, we propose a transfer learning approach Tr-ServeNet to reuse the knowledge of the App classification problem for web service classification. We pre-train a deep learning model for the App classification problem, in which the dataset contains high-quality data from Apple Store, and then transfer the embedded and extracted features to assist web service classification. To demonstrate the effectiveness of our approach, we compare the proposed method with other existing machine learning methods on the 50-category benchmark with 10, 000 real-world web services. The experimental results indicate that the proposed transfer learning method can reach the highest Top-1 accuracy in the benchmark of service classification.