Techniques for Outlier Detection: A Comprehensive View
How to cite: Montgomery, R. M. Techniques for Outlier Detection: A Comprehensive View. Preprints 2024, 2024101603. https://doi.org/10.20944/preprints202410.1603.v1 Montgomery, R. M. Techniques for Outlier Detection: A Comprehensive View. Preprints 2024, 2024101603. https://doi.org/10.20944/preprints202410.1603.v1
Abstract
Outlier detection is a critical technique across various domains, including statistics, data science, machine learning, and finance. Outliers, data points that differ significantly from the majority, can indicate errors, anomalies, or even new insights. This article provides an in-depth exploration of the primary techniques used to detect outliers, categorized into statistical methods, machine learning-based approaches, and proximity-based methods. We discuss the advantages, limitations, and specific use cases of each technique, highlighting their applicability to different types of datasets. The goal is to equip practitioners with a better understanding of how to identify and handle outliers effectively in real-world data analysis.
Keywords
Outlier detection; statistical methods; Z-score; IQR; machine learning; Isolation Forest; SVM; Autoencoders; proximity-based methods; KNN; LOF
Subject
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)