Efficient Classification of Distribution-Based Data for Internet of Things

As an important tool of data mining, classification is also one of the major components of the research of Internet of Things (IoT), which has been widely used in many cases, such as smart cities, information abstraction, wireless sensor networks, and so on. IoT could have broader characterization, where diverse data or information could come from ubiquitous and persistent sources. Influenced by various factors, there are a lot of scenes that the data collected from the IoT devices are in the distribution-based form. Therefore, the study of classification for the distribution-based data is very valuable in the field of IoT. To speed up the training process, this paper proposes a new general approach when the types and parameters of distributions are known. It transforms the original problem into a traditional point-valued classification problem with a sampling-based method. Then for the applications that the distribution parameters are not given in advance, this paper also gives an improved approach, which uses a new Bayesian-based method to estimate the distribution parameters. Empirical comparisons conducted on a series of standard benchmark datasets and a real-world dataset from a major Chinese online travel agent site demonstrate that both of our proposed approaches perform better than the existing methods.