Using modified multivariate bag-of-words models to classify physiological data

Patricia Ordóñez, Tom Armstrong, Tim Oates, Jim Fackler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we present two novel multivariate time series representations to classify physiological data of different lengths. The representations may be applied to any group of multivariate time series data that examine the state or health of an entity. Multivariate Bag-of-Patterns and Stacked Bagsof- Patterns improve on their univariate counterpart, inspired by the bag-of-words model, by using multiple time series and analyzing the data in a multivariate fashion. We also borrow techniques from the natural language processing domain such as term frequency and inverse document frequency to improve classification accuracy. We introduce a technique named inverse frequency and present experimental results on classifying patients who have experienced acute episodes of hypotension.

Original languageEnglish (US)
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Pages534-539
Number of pages6
DOIs
StatePublished - Dec 1 2011
Event11th IEEE International Conference on Data Mining Workshops, ICDMW 2011 - Vancouver, BC, Canada
Duration: Dec 11 2011Dec 11 2011

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
CountryCanada
CityVancouver, BC
Period12/11/1112/11/11

    Fingerprint

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Ordóñez, P., Armstrong, T., Oates, T., & Fackler, J. (2011). Using modified multivariate bag-of-words models to classify physiological data. In Proceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011 (pp. 534-539). [6137425] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDMW.2011.174