Conditional Random Field Feature Generation of Smart Home Sensor Data using Random Forests: IEEE International Microwave Biomedical Conference

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

Research output: Other contributionpeer-review

Abstract

A typical approach to building a feature set for a conditionalrandom 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, andbuild a CRF on these features. We apply this method to an activityrecognition problem on a dataset from the CASAS smart home project, in which we predict activities of daily living from sensor activations.
Original languageEnglish
PublisherIEEE
ISBN (Print)978-1-5386-7396-6
DOIs
Publication statusPublished - 29 Jul 2019

Keywords

  • Smart home
  • machine learning
  • assistant living

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