Earticle

초록

영어

In order to lower the classification cost and improve the performance of the classifier, this paper proposes the approach of the dynamic cost-sensitive ensemble classification based on extreme learning machine for imbalanced massive data streams (DCECIMDS). Firstly, this paper gives the method of concept drifts detection by extracting the attributive characters of imbalanced massive data streams. If the change of attributive characters exceeds threshold value, the concept drift occurs. Secondly, we give Cost-sensitive extreme learning machine algorithm, and the optimal cost function is defined by the dynamic cost matrix. Build the cost-sensitive classifiers model for imbalanced massive data streams under MapReduce, and the data streams are processed in parallel. At last, the weighted cost-sensitive ensemble classifier is constructed, and the dynamic cost-sensitive ensemble classification based on extreme learning machine classification is given. The experiments demonstrate that the proposed ensemble classifier under the MapReduce framework can reduce the average misclassification cost and can make the classification results more reliable. DCECIMDS has high performance by comparing to the other classification algorithms for imbalanced data streams and can effectively deal with the concept drift.

목차

Abstract
 1. Introduction
 2. Concept Drifts Detection with Scenario Characteristics
 3. Cost-sensitive Extreme Learning Machine
  3.1. Extreme Learning Machine
  3.2. Cost-sensitive Extreme Learning Machine
 4. Dynamic Cost-sensitive Ensemble Classification under the MapReduce framework for Mining Imbalanced Massive Data Streams
  4.1. Framework of MapReduce
  4.2. Dynamic Cost Matrix
  4.3. Definition of the Optimal Cost Function
  4.4. Dynamic Cost Optimization Algorithm
  4.5. Weight of Cost-sensitive Ensemble Classifiers
  4.6. Model of the Cost-sensitive Classifiers for Massive Data Streams under MapReduce
  4.7. Cost-sensitive Classification Algorithm Under MapReduce for Imbalance Massive Data Streams
 5. Simulation Experiment
  5.1. Data sets
  5.2. Evaluation Index and Cost Matrix
  5.3. Result of Experiments
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Yuwen Huang [ Department of Computer and Information Engineering, Heze University, Heze 274015, Shandong, China, Key Laboratory of computer Information Processing, Heze University, Heze 274015, Shandong, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605