Distributed Classification for Imbalanced Big Data in Distributed Environments

Recently, with the development of technology, it is quite important to study scalable computational methods for handling large-scale data in big data applications. The cloud/edge computing are powerful tools for solving big data problems, and provide flexible computation and huge storage capability. Moreover, in the real world, the data in big data applications usually are stored in decentralized computation resources, which affects the design of artificial intelligence algorithms. Therefore, in this work, we focus on distribution machine learning, and propose a novel distributed classification algorithm to deal with imbalanced data. Specifically, we explore the distributed alternating direction method of multipliers (ADMM) framework, and divide the distributed classification problem as some small problems which can be solved by the decentralized resources in parallel. Furthermore, based on the distributed framework, we use a acceleration strategy to improve the time efficiency with designing a more suitable model for imbalanced data classification. The theoretical analysis and experiments results show that our proposed method converges faster than other distributed ADMM method and saves training time, which can improve the scalability of distributed classification on imbalanced data.