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A critical overview of outlier detection methods
Computer Science Review ( IF 12.9 ) Pub Date : 2020-10-05 , DOI: 10.1016/j.cosrev.2020.100306
Abir Smiti

One of the opening steps towards obtaining a reasoned analysis is the detection of outlaying observations. Even if outliers are often considered as a miscalculation or noise, they may bring significant information. For that reason, it is important to spot them prior to modeling and analysis. In this paper, we will present a structured and comprehensive review of the research on outlier detection. We have clustered existing methods into different categories based on the underlying approach adopted by each technique. In addition, for each category, we provide a discussion on the advantages and disadvantages of each method. Our paper’s purpose is to assist the novice researcher, to produce clear ideas and to facilitate a better understanding of the different directions in which research has been done on this topic.



中文翻译:

异常检测方法的重要概述

获得合理分析的第一步是检测支出的观察结果。即使离群值通常被认为是错误计算或噪声,它们也可能带来大量信息。因此,在建模和分析之前发现它们很重要。在本文中,我们将对离群值检测的研究进行结构化和全面的综述。基于每种技术采用的基础方法,我们将现有方法分为不同的类别。此外,对于每种类别,我们都将讨论每种方法的优缺点。本文的目的是帮助新手研究人员,提出清晰的想法,并促进对本主题研究的不同方向的更好理解。

更新日期:2020-10-05
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