Controlling Sensitivity of Gaussian Bayes Predictions based on Eigenvalue Thresholding

Han, Dongxu and Du, Hongbo and Jassim, Sabah A. (2018) Controlling Sensitivity of Gaussian Bayes Predictions based on Eigenvalue Thresholding. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 5 (16). ISSN 2410-0218

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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.

Item Type: Article
Uncontrolled Keywords: Gaussian Bayes classifier, decision support system, PCA, eigenvalue
Subjects: Q Science > Q Science (General)
Divisions: School of Computing
Depositing User: Hongbo Du
Date Deposited: 22 Mar 2019 09:54
Last Modified: 22 Mar 2019 09:54

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