Monitoring Acoustic Data Streams Using Possibilistic Aggregation and Multi-label Learning

DOI

10.2991/asum.k.210827.072How to use a DOI?

Keywords

Possibilistic Aggregation, Residual Control Chart, Acoustic data streams, Multi-label classification, Bipolar Disorder Phase Change Detection

Abstract

This paper introduces a novel procedure for statistical monitoring of acoustic data streams supported by possibilistic aggregation and multi-label learning. The primary goal is to learn improved control limits for the residual control charts due to the objective removal of measurements from the non-healthy state of a patient determined through multi-label learning. The proposed procedure is illustrated with real-life acoustic data collected from smartphones of Bipolar Disorder patient. Multi-label classification enabled to distinguish between different degrees of severity of manic and depressive symptoms, and especially for the mixed state - their simultaneous occurrence.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Katarzyna Kaczmarek-Majer
AU  - Olgierd Hryniewicz
AU  - Dominika Kornobis
AU  - Anna Stachowiak
PY  - 2021
DA  - 2021/08/30
TI  - Monitoring Acoustic Data Streams Using Possibilistic Aggregation and Multi-label Learning
BT  - Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP)
PB  - Atlantis Press
SP  - 546
EP  - 553
SN  - 2589-6644
UR  - https://doi.org/10.2991/asum.k.210827.072
DO  - 10.2991/asum.k.210827.072
ID  - Kaczmarek-Majer2021
ER  -