Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles
Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles
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DOI：
10.1109/tkde.2015.2416723
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发表时间：
2015-09-01
影响因子：
8.9
通讯作者：
Bostrom, Aaron
中科院分区：
文献类型：
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作者：
Bagnall, Anthony;Lines, Jason;Bostrom, Aaron
最近，两个想法已经探索，导致更准确的算法的时间序列分类（TSC）。首先，它已被证明，最简单的方法来获得改善TSC问题是转换到一个替代的数据空间，歧视性的功能更容易检测。其次，它表明，与一个单一的数据表示，可以通过简单的集成方案实现提高精度。我们联合收割机这两个原则来测试的假设，形成一个集体的不同的数据转换的分类器的集合，提高了时间序列分类的准确性。集体包含在时间，频率，变化和shapelet变换域中构建的分类器。对于时域，我们使用一组弹性距离测量。对于其他领域，我们使用一系列标准分类器。通过对72个数据集（包括所有46个UCR数据集）的广泛实验，我们证明了通过将所有分类器包括在一个集合中形成的简单集体比其任何组件和任何其他先前发表的TSC算法都要准确得多。我们研究替代的层次集体结构，并证明了实用的方法上的一个新的问题，涉及分类秀丽隐杆线虫突变类型。
Recently, two ideas have been explored that lead to more accurate algorithms for time-series classification (TSC). First, it has been shown that the simplest way to gain improvement on TSC problems is to transform into an alternative data space where discriminatory features are more easily detected. Second, it was demonstrated that with a single data representation, improved accuracy can be achieved through simple ensemble schemes. We combine these two principles to test the hypothesis that forming a collective of ensembles of classifiers on different data transformations improves the accuracy of time-series classification. The collective contains classifiers constructed in the time, frequency, change, and shapelet transformation domains. For the time domain, we use a set of elastic distance measures. For the other domains, we use a range of standard classifiers. Through extensive experimentation on 72 datasets, including all of the 46 UCR datasets, we demonstrate that the simple collective formed by including all classifiers in one ensemble is significantly more accurate than any of its components and any other previously published TSC algorithm. We investigate alternative hierarchical collective structures and demonstrate the utility of the approach on a new problem involving classifying Caenorhabditis elegans mutant types.