Abstract
This paper presents a method of imputing missing data that combines principal component analysis and neuro-fuzzy (PCA-NF) modeling in conjunction with genetic algorithms (GA). The ability of the model to impute missing data is tested using the South African HIV sero-prevalence dataset. The results indicate an average increase in accuracy from 60 % when using the neuro-fuzzy model independently to 99 % when the proposed model is used. © 2009 Springer Berlin Heidelberg.
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CITATION STYLE
Hlalele, N., Nelwamondo, F., & Marwala, T. (2009). Imputation of Missing Data Using PCA, Neuro-Fuzzy and Genetic Algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5507 LNCS, pp. 485–492). https://doi.org/10.1007/978-3-642-03040-6_59
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