inis 18(16): e1

Research Article

Controlling Sensitivity of Gaussian Bayes Predictions based on Eigenvalue Thresholding

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  • @ARTICLE{10.4108/eai.29-11-2018.155885,
        author={Dongxu Han and Hongbo Du and Sabah Jassim },
        title={Controlling Sensitivity of Gaussian Bayes Predictions based on Eigenvalue Thresholding},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        volume={5},
        number={16},
        publisher={EAI},
        journal_a={INIS},
        year={2018},
        month={11},
        keywords={Gaussian Bayes classifier, decision support system, PCA, eigenvalue},
        doi={10.4108/eai.29-11-2018.155885}
    }
    
  • Dongxu Han
    Hongbo Du
    Sabah Jassim
    Year: 2018
    Controlling Sensitivity of Gaussian Bayes Predictions based on Eigenvalue Thresholding
    INIS
    EAI
    DOI: 10.4108/eai.29-11-2018.155885
Dongxu Han1,*, Hongbo Du1, Sabah Jassim 1
  • 1: School of Computing, the University of Buckingham, Buckingham, UK
*Contact email: 1303092@buckingham.ac.uk

Abstract

Gaussian Bayes classifiers are widely used in machine learning for various purposes. Its special characteristic has provided a great capacity for estimating the likelihood and reliability of individual classification decision made, which has been used in many areas such as decision support assessments and risk analysis. However, Gaussian Bayes models tend to perform poorly when processing feature vectors of high dimensionality. This limitation is often resolved using dimension reduction techniques such as Principal Component Analysis. Conventional approaches on reducing dimensionalities usually rely on using a simple threshold based on accuracy measurements or sampling characteristics but rarely consider the sensitivity aspect of the prediction model created. In this paper, we have investigated the influence of eigenvalue selections on Gaussian Bayes classifiers in the context of sensitivity adjustment. Experiments based on real-life data have shown indicative and intriguing results.