ML-KELM: A Kernel Extreme Learning Machine Scheme for Multi-Label Classification of Real Time Data Stream in SIoT

Abstract

Social Internet of Things is the fusion carrier of social network and Internet of Things. In the social Internet of Things, millions of different intelligent objects connect and communicate with each other, and social data emerge rapidly, which puts forward higher requirements for the rapid dissemination of valuable information. As an important means of information retrieval, the research and application of multi-label classification technology in social Internet of Things environment is relatively few. Characterized by multi-labels cognitive learning, fast response, concept drifting and huge solution space, the multi-label learning becomes more complicated under data stream environment. To overcome the problems above, this paper proposed a multi-label algorithm based on kernel extreme learning machine. In the training phase, both Cholesky matrix decomposition inverse method and matrix block method were deployed on the Spark platform to solve the inverse problem of large matrix, thus improving the modeling efficiency. In addition, incremental learning of the model was realized from the two aspects of example increment and class increment. The online sequential extreme learning machine is improved to adjust the network weights and realize incremental updating of input dimension. Meanwhile, the output layer nodes were divided and assembled in the way of “concept group,” which made the single classifier model structure scalable. And the label incremental learning is realized in the output dimension. Experimental results on large-scale datasets and practical datasets under stream environment demonstrate that the proposed method provides an efficient solution to multi-label classification.