One of the more challenging real-world problems in computational intelligence is to learn from non-stationary streaming data, also known as concept drift. Perhaps even a more challenging version of this scenario is when -- following a small set of initial labeled data -- the data stream consists of unlabeled data only. Such a scenario is typically referred to as learning in initially labeled nonstationary environment, or simply as extreme verification latency (EVL). Because of the very challenging nature of the problem, very few algorithms have been proposed in the literature up to date. This work is a very first effort to provide a review of some of the existing algorithms (important/prominent) in this field to the research community. More specifically, this paper is a comprehensive survey and comparative analysis of some of the EVL algorithms to point out the weaknesses and strengths of different approaches from three different perspectives: classification accuracy, computational complexity and parameter sensitivity using several synthetic and real world datasets.

中文翻译：

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极限验证潜伏学习算法的比较分析

在计算智能中，更具挑战性的现实世界问题之一是向非平稳流数据学习，也称为概念漂移。也许在这种情况下更具挑战性的版本是，在遵循少量初始标记数据之后，数据流仅包含未标记数据。这种情况通常称为在最初标记的非平稳环境中学习，或者简称为极端验证等待时间（EVL）。由于该问题的挑战性很强，因此迄今为止在文献中很少提出算法。这项工作是向研究界介绍该领域中一些现有算法（重要/重要）的综述。进一步来说，