Noise Clipping Algorithm Based on Relative Contribution Rate

Authors

Shuoyu Liu, School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
Yueming Dai, School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: This paper presents a class noise cutting algorithm (Class noise cutting, CNC) based on relative contribution rate. The algorithm calculates the relative contribution rate of features to the theme. The most valuable feature set is selected by using features distinguish rating. The corresponding candidate categories for each feature are selected, to reduece the candidate category set, improves the classification accuracy, and speed up the response speed of the classifier. Compared with another ECN noise cutting algorithm (Eliminating the class whose), CNC-has higher accuracy and because of its simpler feature dimension dictionary and better candidate category set, the response speed is greatly accelerated.

Recommended Citation

Liu, Shuoyu and Dai, Yueming (2019) "Noise Clipping Algorithm Based on Relative Contribution Rate," Journal of System Simulation: Vol. 31: Iss. 12, Article 20.
DOI: 10.16182/j.issn1004731x.joss.19-FZ0289
Available at: https://dc-china-simulation.researchcommons.org/journal/vol31/iss12/20

First Page

2721

Revised Date

2019-07-07

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.19-FZ0289

Last Page

2730

CLC

TP278

Recommended Citation

Liu Shuoyu, Dai Yueming. Noise Clipping Algorithm Based on Relative Contribution Rate[J]. Journal of System Simulation, 2019, 31(12): 2721-2730.

DOI

10.16182/j.issn1004731x.joss.19-FZ0289