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- W2895795897 abstract "Abstract Linear Discriminant Analysis (LDA) is a simple and effective technique for pattern classification, while it is also widely-used for early detection of diseases using Electronic Health Records (EHR) data. However, the performance of LDA for EHR data classification is frequently affected by two main factors: ill-posed estimation of LDA parameters (e.g., covariance matrix), and “linear inseparability” of the EHR data for classification. To handle these two issues, in this paper, we propose a novel classifier FWDA --- Fisher’s Wishart Discriminant Analysis, which is developed as a faster and robust nonlinear classifier. Specifically, FWDA first surrogates the distribution of “potential” inverse covariance matrix estimates using a Wishart distribution estimated from the training data. Then, FWDA samples a group of inverse covariance matrices from the Wishart distribution, predicts using LDA classifiers based on the sampled inverse covariance matrices, and “weighted-averages” the prediction results via Bayesian Voting scheme. The weights for voting are optimally updated to adapt each new input data, so as to enable the nonlinear classification." @default.
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- W2895795897 date "2018-01-01" @default.
- W2895795897 modified "2023-09-26" @default.
- W2895795897 title "Early Detection of Disease Using Electronic Health Records and Fisher’s Wishart Discriminant Analysis" @default.
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- W2895795897 doi "https://doi.org/10.1016/j.procs.2018.10.299" @default.
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