The Resource Statistical model-based methods for observation selection in wireless sensor networks and for feature selection in classification

Statistical model-based methods for observation selection in wireless sensor networks and for feature selection in classification

Label
Statistical model-based methods for observation selection in wireless sensor networks and for feature selection in classification
Title
Statistical model-based methods for observation selection in wireless sensor networks and for feature selection in classification
Creator
Author
Subject
Language
eng
Summary
We apply statistical model-based approaches to address temporal and spatial observation selection challenges in wireless sensor networks. For temporal observation selection, we present an improved version of VoIDP algorithm that is the first optimal algorithms for efficiently selecting the subset of observations on chain graphical models. We validate the improvement in sensor scheduling experiments. For location-based observation selection, we address the challenge of placing vehicle detection sensors designed to optimize traffic signal controls by employing two greedy heuristics, entropy and submodular mutual information, based on Gaussian process models. We demonstrate their performance in a simulated traffic road networking map. Experimental results reveal insights of the two heuristics. We also compare the model-based approaches for sensor observation selection, and our experimental results show that the graphical model-based approach is more robust and error-tolerant than the Gaussian process model-based approach. Finally We also apply the submodular mutual information-based selection method to feature selection for classification problems. We compare the proposed method with existing state-of-the-art attribute selection methods through extensive experiments, and show that the proposed mutual information-based method perform comparably with, or even better than, other feature selection methods
Cataloging source
MUU
http://library.link/vocab/creatorName
Qi, Qi
Degree
Ph. D.
Dissertation note
Thesis
Dissertation year
2012.
Government publication
government publication of a state province territory dependency etc
Granting institution
University of Missouri--Columbia,
Index
no index present
Literary form
non fiction
Nature of contents
dictionaries
Label
Statistical model-based methods for observation selection in wireless sensor networks and for feature selection in classification
Instantiates
Publication
Contributor
Thesis advisor
Note
Advisor: Yi Shang
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier.
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent.
Control code
872568824
Extent
1 online resource (xiii, 129 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia.
Media type code
  • c
Specific material designation
remote
System control number
(OCoLC)872568824
Label
Statistical model-based methods for observation selection in wireless sensor networks and for feature selection in classification
Publication
Contributor
Thesis advisor
Note
Advisor: Yi Shang
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier.
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent.
Control code
872568824
Extent
1 online resource (xiii, 129 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia.
Media type code
  • c
Specific material designation
remote
System control number
(OCoLC)872568824

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