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Local-shapelets for fast classification of spectrographic measurements

机译：用于对光谱测量进行快速分类的局部小形

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摘要

Spectroscopy is widely used in the food industry as a time-efficient alternative to chemical testing. Lightning-monitoring systems also employ spectroscopic measurements. The latter application is important as it can help predict the occurrence of severe storms, such as tornadoes. The shapelet based classification method is particularly well-suited for spectroscopic data sets. This technique for classifying time series extracts patterns unique to each class. A significant downside of this approach is the time required to build the classification tree. In addition, for high throughput applications the classification time of long time series is inhibitive. Although some progress has been made in terms of reducing the time complexity of building shapelet based models, the problem of reducing classification time has remained an open challenge. We address this challenge by introducing local-shapelets. This variant of the shapelet method restricts the search for a match between shapelets and time series to the vicinity of the location from which each shapelet was extracted. This significantly reduces the time required to examine each shapelet during both the learning and classification phases. Classification based on local-shapelets is well-suited for spectroscopic data sets as these are typically very tightly aligned. Our experimental results on such data sets demonstrate that the new approach reduces learning and classification time by two orders of magnitude while retaining the accuracy of regular (non-local) shapelets-based classification. In addition, we provide some theoretical justification for local-shapelets.
机译：光谱法在食品工业中被广泛用作化学测试的省时替代方法。闪电监控系统还采用光谱测量。后一种应用很重要，因为它可以帮助预测严重风暴的发生，例如龙卷风。基于形状波的分类方法特别适合于光谱数据集。这项用于对时间序列进行分类的技术提取了每个类唯一的模式。这种方法的主要缺点是建立分类树所需的时间。另外，对于高通量应用，较长时间序列的分类时间具有抑制作用。尽管在减少构建基于Shapelet的模型的时间复杂性方面已经取得了一些进展，但是减少分类时间的问题仍然是一个开放的挑战。我们通过引入局部形状来应对这一挑战。 Shapelet方法的这种变体将对Shapelet和时间序列之间的匹配的搜索限制在提取每个Shapelet的位置附近。这大大减少了在学习和分类阶段检查每个形状的时间。基于局部小波的分类非常适合光谱数据集，因为它们通常非常紧密地对齐。我们在此类数据集上的实验结果表明，该新方法将学习和分类时间减少了两个数量级，同时保留了基于规则（非局部）基于形状的分类的准确性。另外，我们为局部小形提供了一些理论依据。

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  • 来源
    《Expert Systems with Application》 |2015年第6期|3150-3158|共9页
  • 作者

    Daniel Gordon; Danny Hendler; Aryeh Kontorovich; Lior Rokach;

  • 作者单位

    Department of Computer Science, Ben-Gurion University of The Negev, Be'er Sheva 84105, Israel,Telekom Innovation Laboratories, Ben-Gurion University of The Negev, Be'er Sheva 84105, Israel;

    Department of Computer Science, Ben-Gurion University of The Negev, Be'er Sheva 84105, Israel;

    Department of Computer Science, Ben-Gurion University of The Negev, Be'er Sheva 84105, Israel;

    Department of Information Systems Engineering, Ben-Gurion University of The Negev, Be'er Sheva 84105, Israel,Telekom Innovation Laboratories, Ben-Gurion University of The Negev, Be'er Sheva 84105, Israel;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Spectrography; Time series; Classification; Shapelets; Local;

    机译：光谱学时间序列;分类;小片;本地;

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