论文标题
极端验证潜伏学习算法的比较分析
Comparative Analysis of Extreme Verification Latency Learning Algorithms
论文作者
论文摘要
计算智能中最具挑战性的现实世界问题之一是从非平稳流数据（也称为概念漂移）中学习。也许这种情况的更具挑战性的版本是，遵循一小部分初始标记的数据，数据流仅由未标记的数据组成。这种情况通常被称为最初标记为非组织环境的学习，或者仅仅是极端验证延迟（EVL）。由于问题的性质非常具有挑战性，因此最新文献中很少提出算法。这项工作是对研究界的一些现有算法（重要/突出）的审查的第一项努力。更具体地说，本文是对某些EVL算法的全面调查和比较分析，从三种不同的角度指出了不同方法的弱点和优势：分类准确性，计算复杂性和参数灵敏度，使用几种综合和现实世界数据集。
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.
