期刊名称：International Journal of Software Engineering and Its Applications
印刷版ISSN：1738-9984
出版年度：2015
卷号：9
期号：11
页码：91-98
DOI：10.14257/ijseia.2015.9.11.08
出版社：SERSC
摘要：In the IOT environment, sensor data stream consists of event data from heterogeneous multi-sensors. One type of sensor may have quite a different event frequency from those other kinds of sensors, which makes most sensor data sets imbalanced. To classify an imbalanced data effectively, it is necessary to preprocess it for converting into a balanced data. This process may unify heterogeneous attributes in the imbalanced data and alleviate the difficulties for data mining on it. Mass function plays an important role in the fuzzy theory and Dempster-Shafer Theory. In this paper, using a mass function is suggested to process imbalanced data stream. A mass function is developed to compute mass values for imbalanced data sets, and an experiment is performed to investigate the validity to apply the mass function to the sensor data stream.
关键词：Imbalanced Data; Data Stream Mining; Mass Function; Context Inference
