Progress in Outlier Detection Techniques: A Survey

By Hongzhi Wang, Mohamed Jaward Bah, Mohamed Hammad. IEEE Access, 2019

418 citations recorded by the source corpus.

Detecting outliers is a significant problem that has been studied in various research and application areas. Researchers continue to design robust schemes to provide solutions to detect outliers efficiently. In this survey, we present a comprehensive and organized review of the progress of outlier detection methods from 2000 to 2019. First, we offer the fundamental concepts of outlier detection and then categorize them into different techniques from diverse outlier detection techniques, such as distance-, clustering-, density-, ensemble-, and learning-based methods. In each category, we introduce some state-of-the-art outlier detection methods and further discuss them in detail in terms of their performance. Second, we delineate their pros, cons, and challenges to provide researchers with a concise overview of each technique and recommend solutions and possible research directions. This paper gives current progress of outlier detection techniques and provides a better understanding of the different outlier detection methods. The open research issues and challenges at the end will provide researchers with a clear path for the future of outlier detection methods.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/275aaca7621b2ee4ff81cd37162f6c9a90f306e4

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Citations in the corpus

A Unifying Review of Deep and Shallow Anomaly Detection (2020, background): “Due to the long history and diversity of AD research, there exists a wealth of review and survey literature [157]–[176] and books [177]–[179] on the topic.”
Generalized Out-of-Distribution Detection: A Survey (2021, neutral): “Zero-Shot Learning Zero-shot learning (Wang et al., 2019b) has a similar goal of novelty discovery but follows the training–testing scheme.”