QUEST: Eliminating online supervised learning for efficient classification algorithms

Ardjan Zwartjes*, Paul J.M. Havinga, Gerard J.M. Smit, Johann L. Hurink

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
63 Downloads (Pure)

Abstract

In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.

Original languageEnglish
Article number1629
JournalSensors (Switserland)
Volume16
Issue number10
DOIs
Publication statusPublished - 1 Oct 2016

Keywords

  • Adaptive
  • Classification algorithms
  • Naive Bayes
  • Semi-supervised learning
  • Unsupervised learning
  • Wireless sensor networks

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