Minimizing response time in time series classification
Minimizing response time in time series classification
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DOI：
10.1007/s10115-015-0826-7
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发表时间：
2016-02
影响因子：
2.7
通讯作者：
S. Ando;Einoshin Suzuki
中科院分区：
文献类型：
--
作者：
S. Ando;Einoshin Suzuki
提供及时的输出是时间序列分类应用中的重要标准之一。最近的研究致力于探索早期预测模型，即基于截断时间观察的预测。输入的截断可以缩短响应时间，但通常会降低预测的可靠性。早期性和准确性之间的权衡是学习早期预测模型的固有挑战。在本文中，我们提出了一种基于优化的方法来学习集成模型，以便通过直观的目标函数进行及时预测。所提出的模型由具有不同响应时间的时间序列分类器和用于确定其输出的单个时序的顺序聚合过程组成。我们将集成分类器的训练形式化为二次规划问题，并提出了一种迭代算法，该算法可以最小化经验风险函数和同时实现最小风险所需的响应时间。我们使用行为和时间序列数据集的集合进行实证研究来评估所提出的算法。在传统和时间敏感的性能测量的比较中，集成框架在早期预测方面比现有方法显示出显着的优势。
Providing a timely output is one of the important criteria in applications of time series classification. Recent studies have been motivated to explore models ofearly prediction, prediction based on truncated temporal observations. The truncation of input improves theresponse time, but generally reduces the reliability of the prediction. The trade-off between the earliness and the accuracy is an inherent challenge of learning an early prediction model. In this paper, we present an optimization-based approach for learning an ensemble model for timely prediction with an intuitive objective function. The proposed model is comprised of time series classifiers with different response time, and a sequential aggregation procedure to determine the single timing of its output. We formalize the training of the ensemble classifier as a quadratic programming problem and present an iterative algorithm which minimizes an empirical risk function and the response time required to achieve the minimal risk simultaneously. We conduct an empirical study using a collection of behavior and time series datasets to evaluate the proposed algorithm. In the comparisons of the traditional and time-sensitive performance measures, the ensemble framework showed significant advantages over the existing methods on early prediction.