Machine learning: A crucial tool for sensor design

Weixiang Zhao, Abhinav Bhushan, Anthony D. Santamaria, Melinda G. Simon, Cristina E Davis

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step. For each method, the principles and the key issues that affect modeling results are discussed. After reviewing the potential problems in machine learning processes, this paper gives a summary of current algorithms in this field and provides some feasible directions for future studies.

Original languageEnglish (US)
Pages (from-to)130-152
Number of pages23
JournalAlgorithms
Volume1
Issue number2
DOIs
StatePublished - Dec 2008

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Keywords

  • Feature extraction
  • Machine learning
  • Multivariate analysis
  • Neural networks
  • Optimization
  • Sensors

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computational Mathematics
  • Numerical Analysis
  • Theoretical Computer Science

Cite this

Zhao, W., Bhushan, A., Santamaria, A. D., Simon, M. G., & Davis, C. E. (2008). Machine learning: A crucial tool for sensor design. Algorithms, 1(2), 130-152. https://doi.org/10.3390/a1020130