从 BOP 到 BOSS 及其超越：基于字典分类器的时间序列分类
From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers
AI总结：
本文通过解构对比 BOP 和 BOSS 两种基于字典的时间序列分类算法，并引入计算机视觉中的 Spatial Pyramids 等技术，提出了准确率显著优于原算法且媲美最优方法的 SP 分类器。
摘要：
用于时间序列分类（TSC）的一系列算法涉及在每个序列上滑动窗口，将窗口离散化以形成一个词，在字典上构建词频直方图，然后基于直方图构建分类器。最近对这类算法中的两种——Bag of Patterns (BOP) 和 Bag of Symbolic Fourier Approximation Symbols (BOSS) 的评估发现，这些看似相似的算法在准确率上存在显著差异。我们通过解构分类器并测量 BOP 和 BOSS 之间四个关键组件的相对重要性来研究这一现象。我们发现，尽管集成是两种算法的关键组件，但其他组件的影响是混合且更复杂的。我们得出结论，BOSS 代表了基于字典的 TSC 的最先进水平。BOP 和 BOSS 都可归类为词袋方法。这在计算机视觉中尤为流行，用于图像分类等任务。将视觉中的方法转换过来需要仔细的工程设计。我们调整了计算机视觉中用于 TSC 的三种技术：Scale Invariant Feature Transform；Spatial Pyramids；和 Histogram Intersection。我们发现，将 Spatial Pyramids 与 BOSS 结合使用（SP）能产生显著更准确的分类器。SP 比标准基准和原始 BOSS 算法显著更准确。它并不显著差于最好的基于 shapelet 的方法，且仅被 HIVE-COTE 超越，后者是一个包含 BOSS 作为组成模块的集成。
英文摘要：
A family of algorithms for time series classification (TSC) involve running a sliding window across each series, discretising the window to form a word, forming a histogram of word counts over the dictionary, then constructing a classifier on the histograms. A recent evaluation of two of this type of algorithm, Bag of Patterns (BOP) and Bag of Symbolic Fourier Approximation Symbols (BOSS) found a significant difference in accuracy between these seemingly similar algorithms. We investigate this phenomenon by deconstructing the classifiers and measuring the relative importance of the four key components between BOP and BOSS. We find that whilst ensembling is a key component for both algorithms, the effect of the other components is mixed and more complex. We conclude that BOSS represents the state of the art for dictionary based TSC. Both BOP and BOSS can be classed as bag of words approaches. These are particularly popular in Computer Vision for tasks such as image classification. Converting approaches from vision requires careful engineering. We adapt three techniques used in Computer Vision for TSC: Scale Invariant Feature Transform; Spatial Pyramids; and Histrogram Intersection. We find that using Spatial Pyramids in conjunction with BOSS (SP) produces a significantly more accurate classifier. SP is significantly more accurate than standard benchmarks and the original BOSS algorithm. It is not significantly worse than the best shapelet based approach, and is only outperformed by HIVE-COTE, an ensemble that includes BOSS as a constituent module.
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。
