Dynamic Construction of Outlier Detector Ensembles With Bisecting K-Means Clustering-Reference-Cited by-同舟云学术

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Dynamic Construction of Outlier Detector Ensembles With Bisecting K-Means Clustering

Published:2023 Issue: Volume:11 Page:24431-24447
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Koko Rasha Ramadan Z.¹^ORCID,Yassine Inas A.²^ORCID,Wahed Manal Abdel²^ORCID,Madete June K.³,Rushdi Muhammad A.²^ORCID

Affiliation:

1. National Public Health Laboratory, Bioinformatics Unit, Ministry of Health, Khartoum, Sudan

2. Department of Biomedical Engineering and Systems, Cairo University, Giza, Egypt

3. Department of Electrical and Electronic Engineering, Kenyatta University, Nairobi, Kenya

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10058198.pdf?arnumber=10058198

Reference72 articles.

1. Outlier Ensembles

2. Scikit-learn: Machine learning in Python;pedregosa;J Mach Learn Res,2011

3. Identification of Outliers

4. PyOD: A Python toolbox for scalable outlier detection;zhao;J Mach Learn Res,2019

5. On Evaluation of Outlier Rankings and Outlier Scores

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1. Learning optimal deep prototypes for video retrieval systems with hybrid SVM-softmax layer;International Journal of Data Science and Analytics;2024-06-18

2. K-NNDP: K-means algorithm based on nearest neighbor density peak optimization and outlier removal;Knowledge-Based Systems;2024-06

3. One-class classifier based on principal curves;Neural Computing and Applications;2023-06-16

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