Context-aware dynamic data-driven pattern classification

Shashi Phoha, Nurali Virani, Pritthi Chattopadhyay, Soumalya Sarkar, Brian Smith, Asok Ray

Research output: Contribution to journalConference article

8 Citations (Scopus)

Abstract

This work aims to mathematically formalize the notion of context, with the purpose of allowing contextual decision-making in order to improve performance in dynamic data driven classification systems. We present definitions for both intrinsic context, i.e. factors which directly affect sensor measurements for a given event, as well as extrinsic context, i.e. factors which do not affect the sensor measurements directly, but do affect the interpretation of collected data. Supervised and unsupervised modeling techniques to derive context and context labels from sensor data are formulated. Here, supervised modeling incorporates the a priori known factors affecting the sensing modalities, while unsupervised modeling autonomously discovers the structure of those factors in sensor data. Context-aware event classification algorithms are developed by adapting the classification boundaries, dependent on the current operational context. Improvements in context-aware classification have been quantified and validated in an unattended sensor-fence application for US Border Monitoring. Field data, collected with seismic sensors on different ground types, are analyzed in order to classify two types of walking across the border, namely, normal and stealthy. The classification is shown to be strongly dependent on the context (specifically, soil type: gravel or moist soil).

Original languageEnglish (US)
Pages (from-to)1324-1333
Number of pages10
JournalProcedia Computer Science
Volume29
DOIs
StatePublished - Jan 1 2014
Event14th Annual International Conference on Computational Science, ICCS 2014 - Cairns, QLD, Australia
Duration: Jun 10 2014Jun 12 2014

Fingerprint

Pattern recognition
Sensors
Unattended sensors
Soils
Fences
Gravel
Labels
Decision making
Monitoring

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Cite this

Phoha, Shashi ; Virani, Nurali ; Chattopadhyay, Pritthi ; Sarkar, Soumalya ; Smith, Brian ; Ray, Asok. / Context-aware dynamic data-driven pattern classification. In: Procedia Computer Science. 2014 ; Vol. 29. pp. 1324-1333.
@article{229b2dc60340463d8c045110e843609f,
title = "Context-aware dynamic data-driven pattern classification",
abstract = "This work aims to mathematically formalize the notion of context, with the purpose of allowing contextual decision-making in order to improve performance in dynamic data driven classification systems. We present definitions for both intrinsic context, i.e. factors which directly affect sensor measurements for a given event, as well as extrinsic context, i.e. factors which do not affect the sensor measurements directly, but do affect the interpretation of collected data. Supervised and unsupervised modeling techniques to derive context and context labels from sensor data are formulated. Here, supervised modeling incorporates the a priori known factors affecting the sensing modalities, while unsupervised modeling autonomously discovers the structure of those factors in sensor data. Context-aware event classification algorithms are developed by adapting the classification boundaries, dependent on the current operational context. Improvements in context-aware classification have been quantified and validated in an unattended sensor-fence application for US Border Monitoring. Field data, collected with seismic sensors on different ground types, are analyzed in order to classify two types of walking across the border, namely, normal and stealthy. The classification is shown to be strongly dependent on the context (specifically, soil type: gravel or moist soil).",
author = "Shashi Phoha and Nurali Virani and Pritthi Chattopadhyay and Soumalya Sarkar and Brian Smith and Asok Ray",
year = "2014",
month = "1",
day = "1",
doi = "10.1016/j.procs.2014.05.119",
language = "English (US)",
volume = "29",
pages = "1324--1333",
journal = "Procedia Computer Science",
issn = "1877-0509",
publisher = "Elsevier BV",

}

Context-aware dynamic data-driven pattern classification. / Phoha, Shashi; Virani, Nurali; Chattopadhyay, Pritthi; Sarkar, Soumalya; Smith, Brian; Ray, Asok.

In: Procedia Computer Science, Vol. 29, 01.01.2014, p. 1324-1333.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Context-aware dynamic data-driven pattern classification

AU - Phoha, Shashi

AU - Virani, Nurali

AU - Chattopadhyay, Pritthi

AU - Sarkar, Soumalya

AU - Smith, Brian

AU - Ray, Asok

PY - 2014/1/1

Y1 - 2014/1/1

N2 - This work aims to mathematically formalize the notion of context, with the purpose of allowing contextual decision-making in order to improve performance in dynamic data driven classification systems. We present definitions for both intrinsic context, i.e. factors which directly affect sensor measurements for a given event, as well as extrinsic context, i.e. factors which do not affect the sensor measurements directly, but do affect the interpretation of collected data. Supervised and unsupervised modeling techniques to derive context and context labels from sensor data are formulated. Here, supervised modeling incorporates the a priori known factors affecting the sensing modalities, while unsupervised modeling autonomously discovers the structure of those factors in sensor data. Context-aware event classification algorithms are developed by adapting the classification boundaries, dependent on the current operational context. Improvements in context-aware classification have been quantified and validated in an unattended sensor-fence application for US Border Monitoring. Field data, collected with seismic sensors on different ground types, are analyzed in order to classify two types of walking across the border, namely, normal and stealthy. The classification is shown to be strongly dependent on the context (specifically, soil type: gravel or moist soil).

AB - This work aims to mathematically formalize the notion of context, with the purpose of allowing contextual decision-making in order to improve performance in dynamic data driven classification systems. We present definitions for both intrinsic context, i.e. factors which directly affect sensor measurements for a given event, as well as extrinsic context, i.e. factors which do not affect the sensor measurements directly, but do affect the interpretation of collected data. Supervised and unsupervised modeling techniques to derive context and context labels from sensor data are formulated. Here, supervised modeling incorporates the a priori known factors affecting the sensing modalities, while unsupervised modeling autonomously discovers the structure of those factors in sensor data. Context-aware event classification algorithms are developed by adapting the classification boundaries, dependent on the current operational context. Improvements in context-aware classification have been quantified and validated in an unattended sensor-fence application for US Border Monitoring. Field data, collected with seismic sensors on different ground types, are analyzed in order to classify two types of walking across the border, namely, normal and stealthy. The classification is shown to be strongly dependent on the context (specifically, soil type: gravel or moist soil).

UR - http://www.scopus.com/inward/record.url?scp=84902835445&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84902835445&partnerID=8YFLogxK

U2 - 10.1016/j.procs.2014.05.119

DO - 10.1016/j.procs.2014.05.119

M3 - Conference article

VL - 29

SP - 1324

EP - 1333

JO - Procedia Computer Science

JF - Procedia Computer Science

SN - 1877-0509

ER -