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 proceedingpeer-review

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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)
Number of pages4
ISBN (Electronic)978-1-5386-7395-9, 978-1-5386-7394-2
ISBN (Print) 978-1-5386-7396-6
Publication statusPublished - 29 Jul 2019
EventIEEE International Microwave Biomedical Conference - China, Nanjing, China
Duration: 6 May 20198 May 2019


ConferenceIEEE International Microwave Biomedical Conference
Internet address


  • Smart home
  • machine learning
  • assistant living


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