Detecting Outliers in Non-IID Data: A Systematic Literature Review-Reference-Cited by-同舟云学术

Detecting Outliers in Non-IID Data: A Systematic Literature Review

Published:2023 Issue: Volume:11 Page:70333-70352
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Siddiqi Shafaq¹^ORCID,Qureshi Faiza²^ORCID,Lindstaedt Stefanie¹^ORCID,Kern Roman¹^ORCID

Affiliation:

1. Faculty of Computer Science and Biomedical Engineering, Institute of Interactive Systems and Data Science, Graz University of Technology (TU Graz), Graz, Austria

2. Department of Computer Science, Dhanani School of Science and Engineering, Habib University, Karachi, Pakistan

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/ielx7/6287639/10005208/10177747.pdf?arnumber=10177747

Reference125 articles.

1. Astraea: Self-Balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications

2. Learning-based methods for outlier detection imbalanced heterogeneous data;dang,2018

3. Embedding-Based Complex Feature Value Coupling Learning for Detecting Outliers in Non-IID Categorical Data

4. Non-IIDness Learning in Behavioral and Social Data

5. A Comparative Study for Outlier Detection Methods in High Dimensional Text Data

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