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- W3180661779 abstract "AbstractBayesian classifier has become one of the most popular classification methods due to its flexible probability expression and good classification performance. However, in the classification of multidimensional discrete data, the assumption of data independence in naive Bayes classification is unrealistic, the Bayesian network structure is complex and the classification performance is sometimes unstable. In order to more effectively consider the correlation of data in the Bayesian classifier, this paper proposes a Bayesian classifier with multidimensional Gaussian distribution based on discrete data. This classifier uses the moment estimation of the training data to obtain the covariance matrix of the classification model, and reflects the correlation of the data through the covariance matrix. In view of the mathematical model problem caused by the non-positive definite covariance, the largest linearly independent group after feature sorting is selected as a new sample to estimate the covariance matrix, and then the optimized Bayesian classification model is obtained. The classification simulation experiments on several datasets verify the effectiveness and stability of the proposed classifier.KeywordsBayesian classifiermultidimensional Gaussian distributionMaximum linearly independent group" @default.
- W3180661779 created "2021-07-19" @default.
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- W3180661779 date "2021-01-01" @default.
- W3180661779 modified "2023-10-16" @default.
- W3180661779 title "Bayesian Classifier Based on Discrete Multidimensional Gaussian Distribution" @default.
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- W3180661779 doi "https://doi.org/10.1007/978-3-030-78811-7_45" @default.
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