Earticle

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

Abstract
 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

키워드

저자

• Su-Hee Kim [ Department of Computer Engineering, Hoseo University, Chungnam, 330-996, Korea ]
• Dong-Hyok Suh [ Department of Architecture, Namseoul University, Chungnam, 331-707, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605