Name
Abstract | ||
|---|---|---|
Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this paper, we present a deep neural network to automatically abstract low-level representation of service description to high-level features without feature engineering and then predict service classification on 50 service categories. To demonstrate the effectiveness of our approach, we conduct a comprehensive experimental study by comparing 10 machine learning methods on 10,000 real-world web services. The result shows that the proposed deep neural network can achieve higher accuracy than other machine learning methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICWS.2019.00079 | 2019 IEEE International Conference on Web Services (ICWS) |
Keywords | Field | DocType |
Deep Learning,Service,Web Service,Service Classification,Service Discovery | Data mining,Computer science,Feature engineering,Artificial intelligence,Deep learning,Artificial neural network,Service discovery,Web service,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-7281-2718-7 | 5 | 0.46 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yilong Yang | 1 | 13 | 4.09 |
| Ke Wei | 2 | 47 | 5.20 |
| Weiru Wang | 3 | 14 | 2.02 |
| Yongxin Zhao | 4 | 101 | 20.30 |