Abstract
In recent years, with the advent and adoption of cloud computing, different prediction models provided to deal with the same prediction task are simultaneously available on the cloud for companies and individuals. Therefore, the service to provide integrated prediction results from these prediction models is emerging. However, the attributes involved in the models for the same prediction task may be much different due to different perspectives, capabilities, or resources of the model providers. Moreover, these models may also provide different model information, i.e., under different information disclosure level. Although some model integration methods have been proposed in the prior studies, these methods are based on the assumption that the complete data source is available for training. Such assumption is not tenable in our mentioned scenario. To address this challenge, novel model integration methods are therefore necessary. In response, we first propose four model integration methods to deal with the models under a given level of information disclosure by adopting a corresponding measure for determining the weight of each involved model. Furthermore, we propose two model integration methods to deal with the models under different information disclosure levels. A series of experiments are performed to demonstrate that our proposed model integration methods can outperform the benchmark, i.e., the enhanced model selection method. Our experimental results suggest that the accuracy of the integrated predictions can be improved if the stakeholders ask all the model providers to release more model information. The generalizability and applicability of our proposed method is also proved. Finally, when the models are under different information disclosure levels, the recommended way of integration is to only use the common model information for reference.