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- W2013115971 abstract "Data collection often results in records that have missing values or variables. This investigation compares 3 different data imputation models and identifies their merits by using accuracy measures. Autoencoder Neural Networks, Principal component analysis and Support Vector regression are used for prediction and combined with a genetic algorithm to then impute missing variables. The use of PCA improves the overall performance of the autoencoder network while the use of support vector regression shows promising potential for future investigation. Accuracies of up to 97.4 % on imputation of some of the variables were achieved." @default.
- W2013115971 created "2016-06-24" @default.
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- W2013115971 date "2008-01-01" @default.
- W2013115971 modified "2023-09-24" @default.
- W2013115971 title "Investigation into the use of Autoencoder Neural Networks, Principal Component Analysis and Support Vector Regression in estimating missing HIV data" @default.
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- W2013115971 doi "https://doi.org/10.3182/20080706-5-kr-1001.00115" @default.
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