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Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework

机译：根据“监督分类框架”分析稀有事件，异常，新奇和异常探测术语

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摘要

In recent years, a variety of research areas have contributed to a set of related problems with rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple research areas have created a mix-up between terminology and problems. In some research, similar problems have been named differently; while in some other works, the same term has been used to describe different problems. This confusion between terms and problems causes the repetition of research and hinders the advance of the field. Therefore, a standardization is imperative. The goal of this paper is to underline the differences between each term, and organize the area by looking at all these terms under the umbrella of supervised classification. Therefore, a one-to-one assignment of terms to learning scenarios is proposed. In fact, each learning scenario is associated with the term most frequently used in the literature. In order to validate this proposal, a set of experiments retrieving papers from Google Scholar, ACM Digital Library and IEEE Xplore has been carried out.
机译：近年来，各种研究领域有助于一系列相关问题，罕见的事件，异常，新奇和异常探测术语作为主要演员。这些多个研究领域在术语和问题之间产生了混合。在一些研究中，类似的问题被不同地命名;在其他一些作品中，相同的术语已被用于描述不同的问题。术语和问题之间的这种混乱导致重复研究并阻碍了该领域的进步。因此，标准化是必要的。本文的目标是通过在监督分类伞下观看所有这些术语来强调每个术语之间的差异，并通过看这些术语来组织该地区。因此，提出了对学习情景的一对一分配术语。实际上，每个学习场景与文献中最常使用的术语相关联。为了验证此提案，已经进行了一组检测来自Google Scholar，ACM数字图书馆和IEEE XPLORE的论文的实验。

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