A double-weighted outlier detection algorithm considering the neighborhood orientation distribution of data objects-Reference-Cited by-同舟云学术

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A double-weighted outlier detection algorithm considering the neighborhood orientation distribution of data objects

Published:2023-06-18 Issue:19 Volume:53 Page:21961-21983
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Gao Qiang^ORCID,Gao Qin-Qin,Xiong Zhong-Yang,Zhang Yu-Fang,Wang Yu-Qin,Zhang Min

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Graduate Scientific Research and Innovation Foundation of Chongqing

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-023-04593-6.pdf

Reference45 articles.

1. Gao X, Yu J, Zha S, Fu S, Xue B, Ye P, Huang Z, Zhang G (2022) An ensemble-based outlier detection method for clustered and local outliers with differential potential spread loss. Knowledge-Based Systems 110003

2. Hawkins D (1980) Identification of outliers. Chapman and Hall

3. Mandhare HC, Idate SR (2017) A comparative study of cluster based outlier detection, distance based outlier detection and density based outlier detection techniques. In: 2017 international conference on intelligent computing and control systems (ICICCS). pp 931–935. https://doi.org/10.1109/ICCONS.2017.8250601

4. Domingues R, Filippone M, Michiardi P, Zouaoui J (2018) A comparative evaluation of outlier detection algorithms: Experiments and analyses. Pattern Recogn 74:406–421. https://doi.org/10.1016/j.patcog.2017.09.037

5. Tang B, He H (2017) A local density-based approach for outlier detection. Neurocomputing 241:171–180. https://doi.org/10.1016/j.neucom.2017.02.039

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