@inbook{f6b5a55c172547649694fc608599040d,
title = "One-Class Subject Authentication Using Feature Extraction by Grammatical Evolution on Accelerometer Data",
abstract = "In this study Grammatical Evolution (GE) is used to extract features from accelerometer time series in order to increase the performance of a Kernel Density Estimation (KDE) classifier. Time series are collected through nine wrist-worn accelerometers assigned to as many subjects. The goal is to distinguish each subject from all the others in a one-class classification framework. GE-evolved solutions, referred to as feature extractors, are thoroughly analyzed. Each solution is a function able to target a specific sub-sequence of a time series and reduce it to a single scalar. In this way a long time series can be summarized to an arbitrary number of features. Results show that the proposed evolutionary algorithm outperforms two strong baselines.",
author = "Stefano Mauceri and James Sweeney and James McDermott",
note = "Publisher Copyright: {\textcopyright} 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2021",
doi = "10.1007/978-3-030-58930-1_26",
language = "English",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "393--407",
booktitle = "Studies in Computational Intelligence",
}