Conditional Random Field Feature Generation of Smart Home Sensor Data using Random Forests

Mark Eastwood, Alexandros Konios, Bo Tan, Yanguo Jing, Abdul Hamid

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

2 Downloads (Pure)

Abstract

A typical approach to building a feature set for a conditional
random field model is to build a large set of conjunctions of atomic tests, all of which adhere to a small number of relatively simple templates. Building more complex features in this way can be difficult, as the more complex templates needed to do this can result in a combinatoric explosion in the number of features. We use the inherent instability of decision trees to produce a small set of more complex conjunctions that are particularly suitable for the problem to be solved, using the same techniques used in generating random forest ensemble classifiers, and
build a CRF on these features. We apply this method to an activity
recognition problem on a dataset from the CASAS smart home project, in which we predict activities of daily living from sensor activations.
Original languageEnglish
Title of host publication2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)
PublisherIEEE
Number of pages4
ISBN (Electronic)978-1-5386-7395-9, 978-1-5386-7394-2
ISBN (Print) 978-1-5386-7396-6
DOIs
Publication statusPublished - 29 Jul 2019
EventIEEE International Microwave Biomedical Conference - China, Nanjing, China
Duration: 6 May 20198 May 2019
http://www.em-conf.com/imbioc2019/conference/html.php?title=Registration

Conference

ConferenceIEEE International Microwave Biomedical Conference
CountryChina
CityNanjing
Period6/05/198/05/19
Internet address

Fingerprint

Decision trees
Explosions
Classifiers
Chemical activation
Sensors

Keywords

  • Smart home
  • machine learning
  • assistant living

Cite this

Eastwood, M., Konios, A., Tan, B., Jing, Y., & Hamid, A. (2019). Conditional Random Field Feature Generation of Smart Home Sensor Data using Random Forests. In 2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC) IEEE. https://doi.org/10.1109/IMBIOC.2019.8777764

Conditional Random Field Feature Generation of Smart Home Sensor Data using Random Forests. / Eastwood, Mark; Konios, Alexandros; Tan, Bo; Jing, Yanguo; Hamid, Abdul.

2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC). IEEE, 2019.

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

Eastwood, M, Konios, A, Tan, B, Jing, Y & Hamid, A 2019, Conditional Random Field Feature Generation of Smart Home Sensor Data using Random Forests. in 2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC). IEEE, IEEE International Microwave Biomedical Conference, Nanjing, China, 6/05/19. https://doi.org/10.1109/IMBIOC.2019.8777764
Eastwood M, Konios A, Tan B, Jing Y, Hamid A. Conditional Random Field Feature Generation of Smart Home Sensor Data using Random Forests. In 2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC). IEEE. 2019 https://doi.org/10.1109/IMBIOC.2019.8777764
Eastwood, Mark ; Konios, Alexandros ; Tan, Bo ; Jing, Yanguo ; Hamid, Abdul. / Conditional Random Field Feature Generation of Smart Home Sensor Data using Random Forests. 2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC). IEEE, 2019.
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