Cloud services have become a popular and flexible solution for providing components to build service-based systems. A component’s trustworthiness is a key measure that can guide service requesters when making a service selection decision. Prediction of this trustworthiness, based on the component’s multi-faceted quality of service (QoS) attributes, is therefore an important problem to address. In this paper, selective ensemble learning is introduced to address the trust problem for cloud services: We use back-propagation neural networks (BPNNs) as the basic classifiers, with two swarm intelligence algorithms adapted to search for the optimal aggregation weights to create the ensemble: Basic particle swarm optimization (PSO) is used for decimal weights; and quantum discrete PSO (QPSO) is used for binary (0-1) weights. The optimized ensemble learning model, based on BPNNs, is then used to predict the trustworthiness of a given cloud service. Extensive experiments are performed on a well-known, public dataset to verify the effectiveness of the proposed trust prediction algorithms. The experimental results show that our algorithms are not only better than the basic BPNN method in prediction precision, but also outperform current state-of-the-art trust prediction algorithms. The proposed algorithms also exhibit a strong robustness.

云服务已成为一种流行的灵活解决方案，用于提供组件以构建基于服务的系统。组件的可信度是一项关键指标，可以在做出服务选择决策时指导服务请求者。因此，基于组件的多方面服务质量（QoS）属性来预测此可信赖性是一个需要解决的重要问题。在本文中，引入了选择性集成学习以解决云服务的信任问题：我们使用反向传播神经网络（BPNN）作为基本分类器，并采用两种群体智能算法来搜索最佳聚合权重以创建集成：基本粒子群优化（PSO）用于十进制权重；量子离散PSO（QPSO）用于二进制（0-1）权重。然后，基于BPNN的优化集成学习模型将用于预测给定云服务的可信赖性。对众所周知的公共数据集进行了广泛的实验，以验证所提出的信任预测算法的有效性。实验结果表明，我们的算法不仅在预测精度上优于基本的BPNN方法，而且还优于目前最先进的信任预测算法。所提出的算法还表现出很强的鲁棒性。实验结果表明，我们的算法不仅在预测精度上优于基本的BPNN方法，而且还优于目前最先进的信任预测算法。所提出的算法还表现出很强的鲁棒性。实验结果表明，我们的算法不仅在预测精度上优于基本的BPNN方法，而且还优于目前最先进的信任预测算法。所提出的算法还表现出很强的鲁棒性。