Co-eye：TimeSeries多样化包围盒分类的多分辨率符号表示
Co-eye: A Multi-resolution Symbolic Representation to TimeSeries Diversified Ensemble Classification
摘要：
时间序列分类（TSC）是一个具有挑战性的任务，吸引了许多研究人员在过去几年。TSC的一个主要挑战是时间序列数据来自不同领域。因此，在海训方案中没有“一种模式适合所有人”。当考虑整个序列时，一些算法在分类特定类型的时间序列时非常准确，而一些算法仅针对特定模式/形状的存在/不存在。还有其他技术关注区别性图案/特征的出现频率。本文提出了一种新的分类技术，解决了固有的多样性问题，在TSC使用自然启发的方法。这种技术是由苍蝇如何通过“复眼”看世界而激发的，复眼由数千个称为小眼的晶状体组成。每一个小眼都是一只有自己晶状体的眼睛，成千上万的小眼共同创造了一个广阔的视野。开发的技术同样使用不同的镜头和表示来查看时间序列，然后将它们组合起来以获得更广泛的可见性。这些镜头是通过符号表示的超参数化（分段聚合和傅立叶近似）创建的。该算法为每个镜头建立一个随机森林，然后进行软动态投票，使用最有信心的眼睛，即森林分类新的实例。我们使用最近发布的UCR存档扩展版本评估了新技术Co-eye，该版本包含了广泛领域的100多个数据集。结果表明，与其他最先进的技术相比，将不同的视角结合在一起的好处反映在Co-eye的准确性和鲁棒性上。
英文摘要：
Time series classification (TSC) is a challenging task that attracted many researchers in the last few years. One main challenge in TSC is the diversity of domains where time series data come from. Thus, there is no "one model that fits all" in TSC. Some algorithms are very accurate in classifying a specific type of time series when the whole series is considered, while some only target the existence/non-existence of specific patterns/shapelets. Yet other techniques focus on the frequency of occurrences of discriminating patterns/features. This paper presents a new classification technique that addresses the inherent diversity problem in TSC using a nature-inspired method. The technique is stimulated by how flies look at the world through "compound eyes" that are made up of thousands of lenses, called ommatidia. Each ommatidium is an eye with its own lens, and thousands of them together create a broad field of vision. The developed technique similarly uses different lenses and representations to look at the time series, and then combines them for broader visibility. These lenses have been created through hyper-parameterisation of symbolic representations (Piecewise Aggregate and Fourier approximations). The algorithm builds a random forest for each lens, then performs soft dynamic voting for classifying new instances using the most confident eyes, i.e, forests. We evaluate the new technique, coined Co-eye, using the recently released extended version of UCR archive, containing more than 100 datasets across a wide range of domains. The results show the benefits of bringing together different perspectives reflecting on the accuracy and robustness of Co-eye in comparison to other state-of-the-art techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
