Mori Usue, Mendiburu Alexander, Dasgupta Sanjoy, Lozano Jose A
IEEE Trans Neural Netw Learn Syst. 2018 Oct;29(10):4569-4578. doi: 10.1109/TNNLS.2017.2764939. Epub 2017 Nov 20.
The problem of early classification of time series appears naturally in contexts where the data, of temporal nature, are collected over time, and early class predictions are interesting or even required. The objective is to classify the incoming sequence as soon as possible, while maintaining suitable levels of accuracy in the predictions. Thus, we can say that the problem of early classification consists of optimizing two objectives simultaneously: accuracy and earliness. In this context, we present a method for early classification based on combining a set of probabilistic classifiers together with a stopping rule (SR). This SR will act as a trigger and will tell us when to output a prediction or when to wait for more data, and its main novelty lies in the fact that it is built by explicitly optimizing a cost function based on accuracy and earliness. We have selected a large set of benchmark data sets and four other state-of-the-art early classification methods, and we have evaluated and compared our framework obtaining superior results in terms of both earliness and accuracy.
具有时间性质的数据是随时间收集的，并且早期的类别预测是有意义的甚至是必需的。目标是尽快对传入序列进行分类，同时在预测中保持适当的准确率水平。因此，可以说早期分类问题包括同时优化两个目标：准确率和及时性。在这种背景下，我们提出一种基于将一组概率分类器与一个停止规则（SR）相结合的早期分类方法。这个SR将作为一个触发器，告诉我们何时输出预测或何时等待更多数据，其主要新颖之处在于它是通过基于准确率和及时性显式优化一个成本函数构建的。我们选择了大量的基准数据集和其他四种最先进的早期分类方法，并对我们的框架进行了评估和比较，在及时性和准确率方面都取得了优异的结果。
