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

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

doi:10.1109/IMBIOC.2019.8777764

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

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

College of Business & Law

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

1 Citation (Scopus)

146 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 language	English
Title of host publication	2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)
Publisher	IEEE
Number of pages	4
ISBN (Electronic)	978-1-5386-7395-9, 978-1-5386-7394-2
ISBN (Print)	978-1-5386-7396-6
DOIs	https://doi.org/10.1109/IMBIOC.2019.8777764
Publication status	Published - 29 Jul 2019
Event	IEEE International Microwave Biomedical Conference - China, Nanjing, China Duration: 6 May 2019 → 8 May 2019 http://www.em-conf.com/imbioc2019/conference/html.php?title=Registration

Conference

Conference	IEEE International Microwave Biomedical Conference
Country/Territory	China
City	Nanjing
Period	6/05/19 → 8/05/19
Internet address	http://www.em-conf.com/imbioc2019/conference/html.php?title=Registration

Keywords

Smart home
machine learning
assistant living

Access to Document

10.1109/IMBIOC.2019.8777764

Post-PrintAccepted author manuscript, 560 KBLicence: CC BY

Cite this

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title = "Conditional Random Field Feature Generation of Smart Home Sensor Data using Random Forests",

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.",

keywords = "Smart home, machine learning, assistant living",

author = "Mark Eastwood and Alexandros Konios and Bo Tan and Yanguo Jing and Abdul Hamid",

year = "2019",

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publisher = "IEEE",

address = "United States",

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T1 - Conditional Random Field Feature Generation of Smart Home Sensor Data using Random Forests

AU - Eastwood, Mark

AU - Konios, Alexandros

AU - Tan, Bo

AU - Jing, Yanguo

AU - Hamid, Abdul

PY - 2019/7/29

Y1 - 2019/7/29

N2 - 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.

AB - 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.

KW - Smart home

KW - machine learning

KW - assistant living

U2 - 10.1109/IMBIOC.2019.8777764

DO - 10.1109/IMBIOC.2019.8777764

M3 - Conference proceeding

SN - 978-1-5386-7396-6

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

PB - IEEE

T2 - IEEE International Microwave Biomedical Conference

Y2 - 6 May 2019 through 8 May 2019

ER -

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

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