원문정보
초록
영어
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.
목차
1. Introduction
2. Related Works
2.1. Imbalanced Data Analysis
2.2. Belief and plausibility in Dempster-Shafer theory
2.3. Sensor Data Fusion
3. Development of a Mass Function for Imbalanced Data Sets
4. Experiment
4.1. Experimental Procedure
4.2. Experimental Data Sets
4.3. Mass Values
4.4. Calculation of Belief and Plausibility Values
5. Conclusion
References