Earticle

초록

영어

To overcome the drawback of KNN algorithms based on distance measure which did not measure the contributions for each feature accurately. In this paper, a K-Nearest Neighbor (KNN) de-noise method based on likelihood distance entropy is proposed. The relations of feature parameters are used to measure their contributions for de-noise energy, then according to the contributions for each feature leading de-noise of the feature parameters. In order to compare the performance of these relative methods, the Letter corpora and Pima Indians Diabetes data-base are employ to carry out the experiments, the experiment results show that comparing with the other de-noise methods mentioned in this paper, this proposed method have a better ability for de-noise.

목차

Abstract
 1. Introduction
 2. Theoretical Background
  2.1. Similarity Distance Noise Reduction Entropy
  2.2. Implementation
  2.3. Accuracy Analysis
 3. Experiment Setup and Analysis
  3.1. Experiment Setup
  3.2. Result Analysis
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Liu Jin-sheng [ College of computer and electronic information, Guangdong University of Petrochemical Technology, Mao-Ming City, Guangdong Province, China 525000 ]
• Guoxi Sun [ College of computer and electronic information, Guangdong University of Petrochemical Technology, Mao-Ming City, Guangdong Province, China 525000 ]
• Qinghua Zhang [ College of computer and electronic information, Guangdong University of Petrochemical Technology, Mao-Ming City, Guangdong Province, China 525000 ]
• He jun [ College of computer and electronic information, Guangdong University of Petrochemical Technology, Mao-Ming City, Guangdong Province, China 525000 ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605