Earticle

초록

영어

Support vector machine (SVM) is biased towards the majority class, in some case dataset is class-imbalanced and the bias is even larger for high-dimensional. In order to improve the classification accuracy of SVM on high-dimensional imbalanced data, we combine signal-noise ratio (SNR) and under-sampling technique based on K-means. In this article firstly we apply SNR into feature selection to reducing the feature amount then solve the problem of data imbalance using under-sampling technique based on K-means. To verify the feasibility of the proposed strategy, we utilize some metrics such as receiver operating characteristic curve (ROC curve) and area under the receiver operating characteristic curve (AUC value).As a result, the AUC value increased by 4%~16% before and after the process. The experimental results show that our strategy is feasible and effective exactly.

목차

Abstract
 1. Introduction
 2. Methodology
  2.1. Feature Selection
  2.2. Under-sampling based on K-means
  2.3. Support Vector Machine
 3. The Results and Analysis of Experiment
  3.1. Datasets
  3.2. Evaluation Index of Experiment
  3.3. Evaluation Index of Experiment
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Li Peng [ School of Software, Harbin University of Science and Technology, 150080 Harbin, China, School of Computer Science and Technology, Harbin University of Science and Technology, 150080 Harbin, China ]
• Bi Ting-ting [ School of Computer Science and Technology, Harbin University of Science and Technology, 150080 Harbin, China ]
• Liu Yang [ School of Computer Science and Technology, Harbin University of Science and Technology, 150080 Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605