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GACEM: Genetic Algorithm Based Classifier Ensemble in a Multi-sensor System.

Rongwu Xu1, Lin He2

  • 1Institute of Noise & Vibration, Naval University of Engineering, Wuhan 430033, P. R. China. r.xu@ieee.org.

Sensors (Basel, Switzerland)
|November 23, 2016
PubMed
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This study introduces a Genetic Algorithm based Classifier Ensemble in Multi-sensor system (GACEM) for pattern classification. GACEM optimizes feature selection and decision combination, enhancing multi-sensor system performance and simplifying complexity.

Area of Science:

  • Pattern classification
  • Machine learning
  • Multi-sensor systems

Background:

  • Optimal classification frameworks for multi-sensor systems (MSS) remain an open challenge.
  • Classifier ensembles offer a promising approach to enhance classification abilities by combining multiple classifiers.
  • Existing methods like feature-level and decision-level voting have limitations in MSS pattern classification.

Purpose of the Study:

  • To unify classification in multi-sensor systems within a classifier ensemble framework.
  • To introduce Meta-features (MF) and Trans-functions (TF) for describing phenomenon relationships in MSS.
  • To present a novel approach, Genetic Algorithm based Classifier Ensemble in Multi-sensor system (GACEM), for optimized MSS classification.

Main Methods:

  • Developed the Genetic Algorithm based Classifier Ensemble in Multi-sensor system (GACEM) approach.
Keywords:
Genetic algorithmclassifier ensemblefusionmulti-sensor systemoptimization

Related Experiment Videos

  • Utilized a genetic algorithm for simultaneous optimization of feature subset selection and decision combination.
  • Trained multiple classifiers on diverse feature vector combinations and selected high-weight classifiers for the ensemble.

    • Main Results:

    • GACEM demonstrated superior and more robust performance compared to conventional feature-level and decision-level voting methods.
    • The proposed GACEM approach significantly simplified the multi-sensor system architecture.
    • Empirical studies validated the effectiveness of GACEM in enhancing pattern classification accuracy and system efficiency.

    Conclusions:

    • GACEM provides an effective and robust solution for pattern classification in multi-sensor systems.
    • The integration of Meta-features and Trans-functions, optimized via genetic algorithms, streamlines MSS classification.
    • GACEM offers a marked improvement in both performance and system simplicity over traditional voting strategies.