Web Service QoS Classification Based on Optimized Convolutional Neural Network

Abstract

How to increase the performance of Web service classification is one of the hot topics in current service classification research. Web service classification approach based on traditional machine learning and deep learning algorithm are greatly influenced by the data sparsity and bad data scalability, the increase of the amount of data will affect the classification performance. At the same time, the classification result is not accurate due to the change of quality of service (QoS) without considering the time factor. Aiming at the above-mentioned problems, this paper proposes a Web service quality classification method based on convolutional neural network algorithm. The main contributions of this paper are as follows∶ (l) Based on the collaborative filtering algorithm of service, web service recommendation is made from the similarity between services themselves by considering QoS parameters. The time factor of service is considered in the classification process, achieving higher classification performance. (2) A service quality classification method based on VGG-16 algorithm is proposed. In this method, GlobalAveragePooling2D replace the full connectivity layer of the CNN, which reduces the network parameter overabundance caused by too many fully connected layers. Also, unlike the full connectivity layer, which requires a lot of training and tuning parameters, GlobalAveragePooling2D reduces spatial parameters, and its local connections, weight sharing, and pooling operations have fewer connections and parameters, making it easier to train. In this paper, the optimized network and the typical machine learning algorithm and deep convolutional network are tested on the WS-DREAM data set. Experiments show that compared with other classifiers, the CNN presented in this paper has the highest balance accuracy score in experiments. The classification accuracy of the optimized CNN classifier is 98.88% and the balance accuracy score is 99.27%.