A Two-stage Outlier Detection Method Based on Neighbor Density Using Voting

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A Two-stage Outlier Detection Method Based on Neighbor Density Using Voting

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[1]ZHENG Zhonglong,ZENG Xin,LIU Huawen.A Two-stage Outlier Detection Method Based on Neighbor Density Using Voting[J].Journal of Zhengzhou University (Engineering Science),2023,44(06):33-39.[doi:10. 13705/ j. issn. 1671-6833. 2023. 03. 022]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 06 Page number: 33-39 Column: Public date: 2023-12-25

Title:: A Two-stage Outlier Detection Method Based on Neighbor Density Using Voting

Author(s):: ZHENG Zhonglong ¹ ; ZENG Xin ¹; LIU Huawen²; 1. Institute for Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004,China; 2. Department of Computer Science, Shaoxing University, Shaoxing 312000,China

Keywords:: neighbor relationship; density estimation; vote; similarity; outlier detection

CLC:: TP301. 6

DOI:: 10. 13705/ j. issn. 1671-6833. 2023. 03. 022

Abstract:: The outlier detection algorithm based on the nearest neighbor is sensitive to the selection of the nearest neighbor. Too small neighborhood range will increase the complexity of the model, resulting in over-fitting; Too much neighborhood will make the model too simple and ignore a lot of available information. In order to reduce the influence and achieve higher accuracy, a voting decision algorithm was designed based on the neighbor relationship. This algorithm consisted of two steps: density estimation and simulated voting. The density estimation was used to accelerate the density of convergent data to obtain the steady-state density, so that the simulated voting of different strategies could be carried out according to the steady-state density. Simulated voting strategy was the core algorithm of outlier detection based on the improvement of community discovery algorithm, and the importance of data points and the similarity of their neighbors to vote were taken into account. The importance of data points was positively correlated with their steady-state density. The data points with greater importance would have priority to vote actively, transmit their own information to the data with the greatest similarity in the neighborhood, and accumulate the voting ranking of the voted data. After each data has took the initiative to vote, the algorithm stopped iteration and obtained the voting ranking of each data point. The data with lower voting ranking was regarded as outlier. The experimental results on 11 real data sets showed that the average accuracy of the voting simulation detection algorithm based on the nearest neighbor was 79%, which could prove the effectiveness of the algorithm.

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Last Update: 2023-10-22