Lemus Mariano, Beirão João P, Paunković Nikola, Carvalho Alexandra M, Mateus Paulo
Instituto de Telecomunicações, 1049-001 Lisboa, Portugal.
Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal.
Entropy (Basel). 2019 Dec 30;22(1):49. doi: 10.3390/e22010049.
Biomedical signals constitute time-series that sustain machine learning techniques to achieve classification. These signals are complex with measurements of several features over, eventually, an extended period. Characterizing whether the data can anticipate prediction is an essential task in time-series mining. The ability to obtain information in advance by having early knowledge about a specific event may be of great utility in many areas. Early classification arises as an extension of the time-series classification problem, given the need to obtain a reliable prediction as soon as possible. In this work, we propose an information-theoretic method, named Multivariate Correlations for Early Classification (MCEC), to characterize the early classification opportunity of a time-series. Experimental validation is performed on synthetic and benchmark data, confirming the ability of the MCEC algorithm to perform a trade-off between accuracy and earliness in a wide-spectrum of time-series data, such as those collected from sensors, images, spectrographs, and electrocardiograms.
生物医学信号构成了支持机器学习技术以实现分类的时间序列。这些信号很复杂，最终会在一段较长时间内对多个特征进行测量。在时间序列挖掘中，表征数据是否能够预测是一项至关重要的任务。通过提前了解特定事件来提前获取信息的能力在许多领域可能非常有用。鉴于需要尽快获得可靠的预测，早期分类作为时间序列分类问题的扩展而出现。在这项工作中，我们提出了一种信息论方法，称为早期分类的多变量相关性（MCEC），以表征时间序列的早期分类机会。在合成数据和基准数据上进行了实验验证，证实了MCEC算法在广泛的时间序列数据（如从传感器、图像、光谱仪和心电图收集的数据）中在准确性和早期性之间进行权衡的能力。
