Missing data is a common issue in almost every real-world dataset. In this work, we investigate the relative merits of applying two imputation schemes for coping with this problem while designing radial basis function network classifiers, which show sensitiveness to the existence of missing values. Whereas the first scheme centers upon the k-nearest neighbor algorithm and has been deployed with success in other supervised/unsupervised learning contexts, the second is based on a simple genetic algorithm model and has not been fully explored so far.
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