Information-Theoretical Criteria for Characterizing the Earliness of Time-Series Data

• Published in: Entropy (Basel, Switzerland) Vol. 22; no. 1; p. 49
• Main Authors: Lemus, Mariano, Beirão, João P, Paunković, Nikola, Carvalho, Alexandra M, Mateus, Paulo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30-12-2019; MDPI
• Subjects: akaike information criterion; Algorithms; Biomedical data; Classification; Datasets; Electrocardiography; Information theory; Machine learning; minimum description length; Random variables; Signal classification; Spectrographs; time-series charaterization; time-series charaterization; Akaike information criterion; minimum description length
• ISSN: 1099-4300; 1099-4300
• 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.