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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms.

Ardjan Zwartjes1, Paul J M Havinga2, Gerard J M Smit3

  • 1Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede 7500AE, The Netherlands. g.j.zwartjes@alumnus.utwente.nl.

Sensors (Basel, Switzerland)
|October 6, 2016
PubMed
Summary
This summary is machine-generated.

QUEST, an adaptive classification algorithm for Wireless Sensor Networks (WSNs), eliminates online supervised learning. This reduces network traffic and battery drain, achieving high performance without on-site training.

Keywords:
Naive Bayesadaptiveclassification algorithmssemi-supervised learningunsupervised learningwireless sensor networks

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Area of Science:

  • Computer Science
  • Wireless Sensor Networks
  • Machine Learning

Background:

  • Wireless Sensor Networks (WSNs) require efficient data processing to conserve battery life.
  • Transmitting raw sensor data leads to high network traffic and reduced network lifetime.
  • Traditional training methods for WSN algorithms demand extensive communication and human intervention.

Purpose of the Study:

  • Introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for WSNs.
  • Eliminate the need for online supervised learning and on-site training in WSNs.
  • Reduce network traffic and conserve battery power in WSN applications.

Main Methods:

  • QUEST is a hybrid algorithm combining supervised learning in a controlled setting with unsupervised learning at the deployment site.
  • The algorithm processes sampled data to perform classifications, reducing the need to transmit raw sensor data.
  • Utilized the SITEX02 dataset for performance evaluation.

Main Results:

  • QUEST demonstrates a performance penalty of less than 10% in 90% of test cases.
  • In certain scenarios, QUEST outperforms networks trained solely with supervised learning.
  • The solution effectively eliminates the requirement for on-site supervised learning and associated communication.

Conclusions:

  • QUEST offers an efficient solution for classification in WSNs, significantly reducing communication overhead and extending network lifetime.
  • The algorithm's hybrid approach negates the need for impractical on-site training, making WSN deployment more feasible.
  • QUEST presents a viable alternative for resource-constrained WSN environments demanding intelligent data processing.