Comparison of Outlier Detection Techniques for Structured Data
Not on the map. Why?
Abstract (arXiv)
An outlier is an observation or a data point that is far from rest of the data points in a given dataset or we can be said that an outlier is away from the center of mass of observations. Presence of outliers can skew statistical measures and data distributions which can lead to misleading representation of the underlying data and relationships. It is seen that the removal of outliers from the training dataset before modeling can give better predictions. With the advancement of machine learning, the outlier detection models are also advancing at a good pace. The goal of this work is to highlight and compare some of the existing outlier detection techniques for the data scientists to use that information for outlier algorithm selection while building a machine learning model.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 37 references that are in the map. First posted on arXiv 16 Jun 2021.
Builds on
8 of 37 references.
References · 8
- 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 221
- 2018Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Imagescited by 146
- 2020COPOD: Copula-Based Outlier Detectioncited by 143
- 2018Generative Adversarial Active Learning for Unsupervised Outlier Detectioncited by 65
- 2019XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learningcited by 42
- 2018LSCP: Locally Selective Combination in Parallel Outlier Ensemblescited by 31
- 2020SYNC: A Copula based Framework for Generating Synthetic Data from Aggregated Sourcescited by 16
- 2016Robust Contextual Outlier Detection: Where Context Meets Sparsitycited by 11
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Citing papers per year: 2021: 1; 2022: 1.
2 papers.